{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.init as init\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set (97722, 1, 64, 64) (97722, 6)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'SVHN_1x64x64_train.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    train_dataset = save['train_dataset']\n",
    "    train_labels = save['train_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('train set', train_dataset.shape, train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valis set (5679, 1, 64, 64) (5679, 6)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'SVHN_1x64x64_valid.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    valid_dataset = save['valid_dataset']\n",
    "    valid_labels = save['valid_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('valis set', valid_dataset.shape, valid_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52\n"
     ]
    }
   ],
   "source": [
    "c5 = []\n",
    "\n",
    "for i in range(5679):\n",
    "    categ = valid_labels[i][0]\n",
    "    if(categ == 5):\n",
    "        c5.append(i)\n",
    "\n",
    "print(len(c5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(52, 1, 64, 64)\n",
      "(52, 6)\n"
     ]
    }
   ],
   "source": [
    "c5_data = train_dataset[c5]\n",
    "c5_target = train_labels[c5]\n",
    "print(c5_data.shape)\n",
    "print(c5_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yqK3jZrU1K2/HtrcLY5PtY5Yoi1pj7cPvWhSeSy65ZE5zX8p9JtvNLMlsQ/Zz\nm9fJ448//qTlZz7cxzJNm0UnQs5h0f24d7eoQNzTsp/bM5s1zPaZnb2r7Xu5v7docLwX29vmX1uL\n7bsdK6tFWdpWOtELrf5YT3xP5LhmmzBtbUhrFfsdxyzHCK/n2L/00kvntM3B7O/Mx9YQO/aEaVuv\nD7OU2ruo7Q9oyeM7PNNm47J9cidiMd9hO5FNbc/M3xfuvvvujfmz/Wj1Yt2ZzY99mnky3SFKoBBC\nCCGEEEIIIYQ9ID8ChRBCCCGEEEIIIewB59UOtvTUeZM2U5ZFuR0lZpSSPfbYY3OadjDKWRk9gPI/\nysco17LT5lk2SsMoIzx9+vScpg2L8jTK7S677LI5TeksZYEdifv6SfX8f56+znubTJDtwWdgPpQn\nWpQEyhN5X0raTLZn0jiWx+TBJlMn1l8vFgsYsT5jsn2TvncsPnb90uhqnehjZrnoRPUxu1anv3Tq\nrduPbGxbffF6zo+Wj9m4bPwutQua7cEsvkvblZhVwCwzuxAdrPPcR4n2xbbivEsbhNGxg5lV19Js\nQxt3HSuGRV3hOsM9g0XaWX8us8nZ3Me9iFmYWVbacrjPsKheZjmwdl0aadWk9WYH64xlcrFE2Hw6\nYL9gXzA7GPdfbPOOPdcigtnn7NfcM7M/WsQtRimyiEVmyTSr4vr3WRcd+xLLTQsN692sp2Y56Vi2\neV/ei/XF9uZz8bsWiWpfx5e9i9n+zeZjs6SzL7Btr7zyyjlNuxL7lEXGtHde9n/mY9Ytlo19yvqL\njTvrv/bezWvWjxJhXvYdO76D7/Cca/hshNdYuhM5mtieme+YnBMfeuihOX3XXXdtvBf7AZ/F1n3O\n6axPRgejDa3D9u+AQwghhBBCCCGEEMKRyY9AIYQQQgghhBBCCHvABYsO1pEJk45smRKzhx9+eE7f\ne++9c/q+++6b0zxlnNYwSroo+3rFK14xpynBpXyMEi3m8+CDD24sD21o/C7lf8eOHZvTlNdSMmay\nQNYzLWlVZ0cp4/ObRcukd5TDMU+To/M5mb/J7O1UesK6ph2M8j9i9WW2KCvPxSKvXRphySwnrBuT\noNv47UT1sjLwXuxHZieye1k9mNWlExGoY69b70dmieFzcr7rRO/qwPtyrFkkAn7O8vD5LVJOZ6yR\nju3HZNxmqdqFCERkaSQls/yyDWk1uO666+a02QHN+tOJSml2QNqcjx8/vvFZuKZT/sw078XIG3xG\nXmOWC/bgm5HNAAAgAElEQVT9dZsFZeo2zvkdyrNZd6985SvnNKOQmiWAexGWz6Ty/K5J8Xk911Cu\n9ddcc82cpjWd6z7z4TOahe9iweatTqSwToQ3ftf6KvdrhOOC/YXtZlGwOlYs7rOYZt+x/ePVV189\npzn2zf5JW41FK6vy/bdZPzhG+PycO1760pfOac6hzJP3YhlsPeIzsP34nHzHYBtwvNt7i0U1sqMV\nDNsDHdYG20LHbky4NrG+zZ5r1mnaG23Otn0pr+F9bV9m7yI2jjrvLnyuTqQ4O3KBc0LV2Wsi1yB7\nH7A1y8rN6/keyndsvvOzXviOzfFuWERkvmPz6Jl77rlnTnMNtUhvfD9lOdk2Fj2N836HKIFCCCGE\nEEIIIYQQ9oD8CBRCCCGEEEIIIYSwB1wwOxjpRGGi5Irwc8pcKcX6wAc+MKfvv//+OU1pJOXMlK1R\nqklLF2XzlHAyH8rQaAE7efLknKa0k1Ivs8lQMka5KOVjZn1YPzWc9UJZKfOldI2SdcrzKJGl3Y6S\nVEtTekdpHDGpNOuLVgE+p0lzWV8mp+7YRjqRe3aBpdGFOvVhEf06drCOhNVsBybB7pS/E/mqI+Nn\nvzNZsslr1+9h0dTMamHXs0xm77F51u7LsWx2LX5udgird96L86lFGOS4tshou4ZJx63vWXRErl/k\npptumtNvetOb5jTr+z3vec/Ge7ENbY63NufacuLEiTn9RV/0RXOa0uYHHnhgTnPdNFsKo5PQys2o\nHZSB33LLLXOaknvuGaqq7rjjjjnNtY/3Nnk25xHe73Wve92c5vrFscO+zQg073jHO+b0e9/73jlN\nOwHhGGc98lloAXv5y1++Mf3Od75zTrOvWPSajr2W7Np6aravzvV8Vlv7LNKQRa+yKF22brKt2P9t\nb8k9I2F5WGazPvAaKyf7su0rqs4ea+vHH2zCIjBxX0q7Gq+3NZTwcz4b65T1QlsR50eWx+zu/Jxz\nCG0jnX65dJxuKzam7D3Aou+xjjnumL9F6yNmI7YIzOyPFlXWLMgWxdH2d9YvOpEkLf91OxjHI+/H\nPm9rFtuMcxPnOPZ57hUYmYvv4Wwz6wd2bIjtdbgO8j2fEcHN1ssxzme0KHd8XrOFdtjdER5CCCGE\nEEIIIYQQ2uRHoBBCCCGEEEIIIYQ94ILZwUziTkxGTvkZpVJmS6IsizJtSrEoAePJ4pRx0WJGKTdl\nXHwWWpQoSWOetI9RPkdJF/OkDPz666/feI1JONflsWYHo4Xg5ptvntNmz7MT0ZmPyQ2vvfbaOc32\nsAhn/Jx1xDZjGVhOtjHlhfyccs+LxerVwZ7PxqlJyi3iVWe8m/3O5N/WPrQJmh2KmBXJnsWe3dLW\nfw/rX2bPM6sA5aOsL8pHzTbD/JlPxxJAuTDHoEnxWY9sD2t7PiPzpGyY0TiYtnnQ2mBb6YwXi9Jm\nabYt5zzaNCyyokXo43xskXnYN2njY7vRfkHYbpS+m5WbNhPamLjOMEoRLVDcY6xHB6M9jM/J6/i5\nRWNi+Wj9MCspn98k9NYnmLZyco1mOdkeLKdJ90knItguRw2zOX/p9Wwf1r1ZE9j+HL8c48zfohTZ\nHG/WKPYRa39+zv7CNOcZXs9xZ2Vjeh1aJGj7NOu12cFYPh6DwOv5vmHWNcL5jmOKcx/z5+e0GFlk\nLjsqgWu9zSe2v7lY9r0WaY51wHbmGsE6MHsrxyPHbCfqrkWA5DVmJTNrWMf634kuy7WL5Wcf57Mz\nT75rV509Hlk+zi/Wz22O4DVcy7hG07592223zWmOQe4D+I7NudUikbN++cwsA/fGfG9nnfJZrJ0s\n6qgdgdIhSqAQQgghhBBCCCGEPSA/AoUQQgghhBBCCCHsAVsRHYzYCdyUN1IORhkbpWuUQDJigNmD\nmOfdd989pylJpaSL+VPOxzQtVrSGmVWCsnbei+WkpI4yMYOysvVTw3kPRo6hLI11ajIzlpvPzOek\nbJESO37XogWxT1gkI96LNjf2IcqRTXJNaaKdjH8x2sQ6z2c2GrPuWf6Gtb/di9dzfjAbC2XAHZmu\n1QP7BecNls3k52ZlXZfjmjWD+VqUEObL8ci5g7JVjiPWESMg8tkozWVd33PPPRvTzJ9pi3hB7HPK\nhlk2zi0sp/XvXbODWX2YXJxQyt6J4ETY11gGXm8WKMK5lvMxJeVmUzDZvD0v+witXrR6mM3xMEm1\n2V+tXvg8tITweo5HrsWsR6s7s4lZhD7uXThv2jpoNmqT6xPmb1aliwWL6Nj5nJi1iGnO0xY5y6Jk\ndizMnB9ojWF72hrHPTO/yz7Lz22sWeQy9vF1qybztQhqFinKovaYVZPXWz1yjDAfWr34ubUB04R9\niOOLY9z6nO3bzIqyy3CMcA7m5zyagu3WGb9cB2zts30m8zF77no/PwPXZVujuTew6HZmHeZ3Oa7t\nXYDlZ4SuqrMjV7KOuDYzUqnZJG3eYZ7c99IqxXHBscw1l3ta3svanvflOs53Ums/229x3Nn8yLq2\nd54OUQKFEEIIIYQQQggh7AH5ESiEEEIIIYQQQghhD9gKO5jJYik5M3kepWiUetH6wM8p8zx27Nic\nppTu9ttvn9OUd9E+ZdG7KJ977LHH5jTtYLSn8aRwys14PdMsAyVmdqq8ycCrzpbrsUwWqcJsJsyH\nsjqrI0Zu4/U33HDDnKYcnc/ANOuF7U0rCvsK25IyP8pxaYGh/NHkkhcjFgXMLEd2ar7Z6cwKYPmb\nFdSuYdQOiyJkdjBi9g5GFbB6MBmpWanWI1lZpAfCe/AaylM5Rmhz5bjjvSg75+c33njjxmfgNZxf\nOE+xPVhf7B9m+yE2FzGag60lZBcsYB060cEsqibXQc61bH/KqPldrnG8F/szr7FoQZR7m/2oE4XQ\nLA7MhxYAs3Byvud8sj7fL7Uy8dm456AtjWOW45Rj6vjx4xvz53Oyrjn3WTvxOc0Cw7pjmpYk1ikx\nu4pFOdy1SGEdS5f1l4710iJ28XNe37F6sb75Xc67dl+zhxP2R/Yp6zucZ2x9YJ5mN1vPl9+hdcv2\nInxmi2pm1ml+l3XE+3KdogXG9hPMk89M7N2Ddfroo4/OadtzWLQqG7O7tgfmusb5mP2f/Zx1Y9Ea\nCa9hHdu447zB622vZ5E9iUWXMju+Wf06xz7wczuK4w/+4A/Oyvdd73rXnOa7GCNQsw3YZmaZZJ83\n+xXzYRQwrq12LInNoewHfHc2KyDHss1lxNZNYvvnpUQJFEIIIYQQQgghhLAH5EegEEIIIYQQQggh\nhD3gvNrBOpIlXmPyV5M503508uTJOU2ZGG0NlKERyjPf/e53z2nKKnkvk4bRokR7BCVglHjTNnH6\n9Ok5bZHFKGczOTbvux6VjDYuStQoc6Xcls9JSxfrmvezMvG7TN96661zmif1m+Tv3nvvndOnTp2a\n07ThUcLH+mUbnDhxYk5TRssyUDZvmHRyF7AoN2adMesWMZk664Zj2SLRmS2PkkmOTfYpyjzNPmPW\nMD4j5x+LlMVymlWLfZn5rMtI+WwmGbXIfxyDtEbeeeedc5r1ZREgTIJMibtFVyEWndEikrDNWAaz\nFVnEMYvSsmt0ooOZBYx1w/FISTxtSZz7mTZLSyfyG/uOWYu4/nTsehYtxJ6XrEfJ3HTfwyJvdCxA\nFo2JEcv4/BZVhWuW2UbMDmZ7FEubFJ/jmmmzJNkY71g1dw0bFzYP2XfZDhwvbBOze3SwoxWYNmvM\nUvssv8v9o1m1uF6ZXcXsFOtrjkWHZJp7ObNqs6ycH/lds4OxLVk+5kMrLMc190M2n1g0OH6X6zvn\nE5sTrC8y/12zgLG8ZiFkvZpdi/ujTtQ/1p9ZySwCl9238z7BfRaxCJ5c12x8cX6w6JnsX3/4h384\np/nuXHV2dDCLwMV3ctaFzUe0ldmeljZM7hv5nmB7EbPL2nsLy8ZxzfdW3sv2pWwzPktnL2j7HmO3\n3lRDCCGEEEIIIYQQwlMiPwKFEEIIIYQQQggh7AFbER2MmCSPUixKopg2axjzoTzzqquu2nhfk0BS\nCsp7Ua7F+zKCFuVzjEBFy5FJU/ldSv4oSeucWs/rq86uF8rSKA3k5xZ9gM/MuqNc1uqOUjqWz9qe\n96LEjhYwwvZjXbBtKEc2y5D1OYtUt2uw71lkHKsPqwPrRxZRyuScHKfMh/2IklRGKOCzsL+YrNei\nKbEMJhXnmKWklM9itkJG66o6W75MC4nB8UgrKSMN8XOLZmF1ZJGcKL/nNZS8sm3MfsJ25dik3Jd1\n3Yn2YrYHtscuRCPqjDs+k0nQWQdsQ4vMZdFSOO7Yn1kGztnMk/2Ffbxjb+Gzm0zb1nHWCfs41xOz\nXXJsVbkd3WxWvLdFBuE8aGscrWEWgYj5cz41K4LN18yT/YZ1auOX8Nkt6p+xC+tpxw629LudqF6d\naKUdm3bnvnyWjiWIfcHsg7zG9qhmEaadh33wsHuw73HOMusT5ymuZdx/mx3SbGUW7Yi2Ee6HzRZt\ndh2LgMXPObfwXkttTrtg7bS9Bvskn4/1ynpif+EY5Of2fsD+zM8temxn3HXsZuw7XDeY5jOaPZyw\nj7M/co3iXo/7zSqvi040LuuH9k7Ka7gv5Vjg2KFljGPW7IKEZeNcwftyLbbo3dbeHUu17Y06RAkU\nQgghhBBCCCGEsAfkR6AQQgghhBBCCCGEPeCC2cEorbKIPPy8E52J0jhKHZkPpaSUgJkVi1Iskwia\nvWldRn4Gyj+vv/76Oc06YTkJ7RGUpppkkeWhFLDKrS+US7IuKF2zKGWUVNJCYxYPk7+yLdkneD3l\nhrR0scx8LrYH86GVzKxBJsXetShgHexZO5hslVDCyT7FfsRrKFXl2CFmMWObM8oepaBmj6Bdg9dz\nHuDYJ8zTIhkxchcjCVadLSU1CajZG++///45bRJZk76zPficrDtaWC36CaMesi0tEpn1G85xFonN\n0h2L0cUyfi1KF2Gb08pgUXvMgs3+aHJm1r1ZIjg3d+YNw9rZys91huOOfZNlXq9Pi7Bia5nNKUyz\n7rhOc/xyLLC+zMJnVmjbbzEfi5DSWQ/YHjYPdqKc7AL2HGZ/Nmwv1rGb2djh2mRtYpGyiB3RYOOU\n/ZrzjFkxzN5iVlOWf936wHwtopDN+fyuRf4yOyfLwedhPrSKcK3k58yHz8y2tChxXIstyiP3RpxP\nzM5k43cXYF3aHGbrpkWpMytSJzoz87T2t+iWdkQJ11++xzByMtc4W6+OHTs2pxmhyyyMnAe4RnHt\nWq8THoNCzH7ViUDMtlk/7uQMfH+45ppr5nQniqFZyvn8vJ779quvvnpOMxIZxyzLb1GEbd9u6XWL\n7JNxceyAQwghhBBCCCGEEMKh5EegEEIIIYQQQgghhD1gK/R9nSgQlESZXcVsB3Z6NyV5zJPyLsq+\nLDKXWVHM3kRJqUUHozyNz07pnUUPIJQIUpq4/n2rI0rLKINnXiwTJamUGPIZeFK62cpM7kwr2cmT\nJzeW51WvetWcppTXIiJRwsjPzSZEzNa4a3TkwEs/JxbJiH2HfZX9mRJOyh5NOm19k32NY5BtSKk1\nPzdptmEn+vNzSkRPnTp11vc5B1mdshx8ZtrBOE+Z3J1jlnMc64t2MMqLGbmMVr1XvvKVc5pzGeuR\nc4tZFyhZNtsSJcvM0ywQF8uYJWYJsXFqfb5jpzOpvEnZuZ6wLzB/ftf6u1nSzDrM/C1iC9ciWirN\nKl7lEXY4driO8Pk51jiXcVxw3LFMZnFn+5ktgWW2iF0sJ8cUrzHrjvUzfncXIgotxcaaYWPKLD6k\nExGPWHksgp5FeLPydKIdWYQusz7YEQe0dFgUvsPKarBOWS9mDbKoPZbmfMrxxbXM7CEc74R1YZEB\nzQ5ma2InItiuWafNvmZrlu3rWDe8xqJncn3hkRXc3544cWLjdzkW7D2M13B9eOc73zmn3/Wud81p\njimLoM31yqK8sgzsF1yv2GfXj27g+zbvxzFidjAbX8yHZeK4tuNXWB6zx9ucxTJwT8PPuYdgPTJt\nlnCLQGpHVNh7UYfdGtUhhBBCCCGEEEII4SmRH4FCCCGEEEIIIYQQ9oDzagczOSE/t0gEdj0lY5TJ\n2WnZlH2ZzNkibFCixXtRJsZrmDYJq0nJKBnj81IiaxE/LGrQgw8+WIRyQN6DdUT5GfOitJFyQ1p3\nbrzxxo3lvuuuuzZ+16JDsWy0g7EMlAVS8kfpLGV1tOJYxASTIHZOrb9YMBvc0s9ZTxbRYN2ueAa2\nW0dSbhZDs8DYXGHRJSi75lixCBQWDY0RHCgbrjpbLmxQesuxyT7MMlHmyzQtXcznj/7ojzbmSWsY\nxxqtrbSCUnZrcmrORRybnH/MAkZJPNvVxumujVmL5mTXmIS/Y2ntRFSxCIrs21y/OAdzvBCuDxZl\nydZiftfsgByDXENobbRonuvRNkyqbTYDi5ZE+Ay0J3M82n6C85fJyzkP8l4cR2ZXYRt0bDKEZWP9\n2HpqdpVtxWyJLDvb3PokxwjHI+c8i5JpUWt4L6bZ5oxgYxHhzFZj9yLMk33W9lwcj1zfuE82e07V\n2X3V9ujEooDx3txPcJ/JNMcU0ywP10GLXMY9+gMPPDCnOa4tGhHriG3Mzy3SFdvb7Osd69S20lkf\nrZ+b3Zb9xfaf3ONxr2vWdos+x3LaMQC8F/doNmez/9oxI7yv2fTtGA/aoarOnmv4fT4nI0rbGOH4\n4r7RjhpgOVgGthOv537SLK9my2J92TEF9q7Ne9lewiz9HXuwESVQCCGEEEIIIYQQwh6QH4FCCCGE\nEEIIIYQQ9oDzagczCbPJ9s2Ow2tM8mqSKJOSUnJlp+Ob/cokwZY/n4X1QCkgpdkWtcHgvSjzo7R8\nvXxWJmLyQYviQEsIpXeU0lEWyHysfi3aE/sQLSqUTdMGQNktJYVMd2wYZNdsJmSpvctsm9Z32Ccp\n/2Z0HqYpkzTZuUUPoCyUY+fmm2+e05Tj8lko2+R3OTbt1H9ez7HFfs1npAWMfbnKJaCE9chxwesp\nQac9k2OTElnK8TleGHGMn/O+7P+UwloUKLaxRXykBYZtxvzZNp3+dzGydO6xtczqj9hazHHKtuIc\nzDFi9qZOFCmuRSYDJ5zXOdaYNmvbui2pUz6Tr5s039Yafs7ntD2QwXWW92U72VxmY9NsI5218mKH\n/ZPznNmVzAbC6832amuFRb5i/mbV5Dxgc0JnzjEbi9nDzZZi0e3W4TPbuwHnCLPlmA2KY5nt1zm6\ngve1qHl8fq6zZlUzizv7illQiUUqJEsj4W0TS+1gfD6zSHei095xxx1zmrYpHptx3XXXzWmOTYNl\n4x6QdjOWh3slizhntif2Iz6vHUXA+9LaVVV1/PjxOc3xzHHHvSjrwvYHFh3MjmywecDmVnsntT0n\nn9+s2UzbUTidPZaNwaV7wSiBQgghhBBCCCGEEPaA/AgUQgghhBBCCCGEsAdsRXQwu8bsYIbJXymP\nooyNkrSOnNlO5jb5tkURMXsEv0sJm50gbvVpJ8DzedevI/Y8zIvSRubDSAS0ATACEWV4lE6ybSh7\no8SO9zWryGWXXTanKUmk9JnPyHzsudi39gnrbyYNtr7DtqUlirY8WjPM2sn8rWz8LvvCm9/85jlN\n+SfLyX5EKPlkniY1tT5FCxhltJSdrmMRc5gvIwqxLjgGaQejZZIRRiiFZZQuiz5G+TrbmPVoclaL\naMBr2MYW8cTaoBPdbxdYatU0y6TN5VbfZmE2ixLbkHMt05xH2Wd5X1sHOjJwi/jJclrUIJbtMOuD\n1a/ZZjiv8Zk5R/De7Ofs253oWkyzvji/8BqOU6bNDtOx41raZPCWzy7A+uDzdY4ssD0F29zsGDan\nWlQvi5rI8vAas6TZ0QRmhzLrjV1v/dpslOv2GT4P645jivlaRC37Li1anSiG1v9t78LnMfucRYMj\nzId1Yp93xu/S97FtwtY+YlGhWU+2vnBPdOrUqTl99913z2m2s607LIOtfRbBlv2Uez3uV7n3pvWf\n/Z1jwtYErl3Mk++YXPfXy2R2Wb4ndt6Tbb3j2OE+me+bvMYisdl9bV7uHKViexfbY9nYtzG4NHJf\nlEAhhBBCCCGEEEIIe0B+BAohhBBCCCGEEELYA86rHczkh3aNSaXMlkJJJmVZlK5Rjk2ZmJ1EznxM\nSmkSaZOg81ks6oFJc01mbLJ/k5RWuZ3MIheYtYzPQ2kf5YmMPMJyU6ZOSaVFWWMZ+Dmlh3Zflo11\nYdHBjmIHsz66rZilwLDn43fZVrQTPfDAA3OaklSOTUa1Yt135g2TyDLyAD9nH6fM0+yS/G7HdkrL\nFO1gZuFafx7CsnK8MM28WI+0RlJ2y3FB+S8tY4wOxmewaC4sAyMpdCTx/JztwTHOcU15dGeN2WVM\nMkxs/PK7nDs70WAsbe3JNmcftEhAHGtclzsRadj+7Efsj2aNMXk/OWxsmmSd9WtWHFubTf5NbI9i\ncnTel/sJjiO2GWE5ra5ZTrP02/7GyrkL49dsQDYnEWs32jE4/3X2gbZv5Bzfsbp0Iu0anXmJ5eR6\nvdSqu163FtGTdcrnZP9nmutgJ1Jep46WRju2PSrHI+dfltPeDWwfaza8jkVlW7G9mUUAZv1x/Fqf\n5PrCPdHtt98+p2mpZ1TZznul2Ut5Pfd0r33tazc+C6+/66675jTblnsrs9rb+6yt1+tWTe6bOb4s\nWiHvYVH5bDxyb3Hy5Mk5vR6Fd9N3eYQCsXdsW8vsPdz2W2aX7UQl7+wZjCiBQgghhBBCCCGEEPaA\n/AgUQgghhBBCCCGEsAdcsOhgJic0eblJsSwagEmbaZugHYPyNErWKdsziWhHas7nsog6hHVFCWfH\nTkFpG8tAieP6/1vECLP3mB3MZL6UFbKszIftYfI/Xs9rLIKFyakpmWN7W5Qmk+qZ/WQX5LJHwSxg\nrAP27YcffnhOUyJLaxjrnvXNPtyRUbPfsS9w/FJ2yvakZJX9zmSuZrFhFBFGQKNs+LBICiZB5j0s\n+qDJ4HkPizrEur722mvnNCWyfAbOCRy/rDuzYdq4Zp6coxjlgu1kFhWyaxHBDHsOs9N1Ikia3Jh0\notOwndkH+TnnBEqzOfYpL2d/tMgY6+vapus59i1qJfsg+xfTVWc/M/u8SbVtnbXoL5ZmW9p6T8yi\nZe3EuubY5FxmkUptf9aJKGS2mqU2pAuBWZXNcmU2K/YRsyXxGrObMW1Rxmwts6hDZmfrYPtBe3Ze\nY9HzDpvL7XksCh7XFDs6wCKPHsWu2LHach1kXTDdsaXYe5dFFiO7bAezNYv12jmCg/XHeZHrF21W\n7373u+c0I2cxCqtZODkHs5x2PS1m9v7L9ZRl5lEMHCtmT7MjTcyCvD5/WyRNG7PclzLNvQI/Z1sy\nCtgdd9wxpzn2bd7kNaxHs1uyn9k8Ze9LZjXt7LcsHTtYCCGEEEIIIYQQQvg88iNQCCGEEEIIIYQQ\nwh5wXu1gSyWEJjc2qa3J2Cgfo3yb0nSzeJjlxMpJGSmlbZT20R5Caxi/S8wOZnJUq6t12R7lgyZr\nZl4dC4F9bvJaXs/2MBm8RUkgbCeTTXdOXycmWb9YbCbE2t+e1WSPFvGHEtnHHntsTptUvhPRwmwE\n/K5FSzErFb9r0X4sIhD7MiOjMcoW+zJtKVVuLbDxb7JwStxpB+O4YBtYlATK5s0aSakxn9+ig7FP\nsC5M7m72BpNuE9bJUSIpXGhMUs065npnNgKbR/k587drWB7K3SlTJ6xvWrMtAiTnBH5utl2zBdPS\nxXLeeOONc9rsmRxDVWfPX7S52vxCGT3nArO7cC7gmDK7gsn9+TnLwLHPccQ6ZTn5ue2T2P9oH7OI\nO8TsvrsQHcz2Ap3osWxzzklmY7L10exKlmb+tt6Ztcj2wBZlrLNXsrKxz3byX/9/qztapC06mNXL\nUmtcxyZJLH+LCGb3ImYbMdvwLli9lmKWGs5tHVs5+yTXL1qrOHfa+2Nnz2LWYbM5c81l/txzWp5m\nySS8r9mnuOasY+sC4Rg0+6FFMWTb8B2b13Bc27zDKML23mp0Iox25hDbo5olvjO3GFEChRBCCCGE\nEEIIIewB+REohBBCCCGEEEIIYQ84r3awDia56kQ5sUgylKtRMmZ2MItsQ+m0RQpjGSg7ZT6UxVFS\naDYTk8IRu4byt/WIabyOaZMDmmWjExHNbCwW7YiyRYvSZKeym2zanpflMbmdcbFIZzuR5sx+ZSff\nm1yWFki2udkFOtJ3s0Cy71h0g07+S/sCLVaMgEapMPv+ej8ym5LZJ4nJ/TlOrb74/GYl41hjGax+\nWWaTx7OvmCXJ5PqdfrkLkYaMjlzcokwQ1gdl8CY9Ntsf24d9gXJ0WpqsTWjvsnISs0HYHGLRUrgu\n0xrGfNjfeX2VR9OzfQAt6OznfE6Wj3sXzpUWwYVj0CKPsl1tT9CxeDN/wme3drJIbxc7ZssiFrGL\nacOOR7C1zKK2Mm1HENiz8Hrbo9p6bfZls5ww/3ULVOf4Bj4nbaIWQczsJzZP2X1tL9WJLmxpm6Nt\nD2R2MLOTdKJRbiucY2y9s4iQNlfZWsY08+Fczvm7Y5nk2LeobtaXrZ+aDdysoNaPzHbJMvM9t6rq\nAx/4wJzmc9J+xTqydcTq0Y6fYKQwex+85ppr5jQt1dzHsK5tHFmZbU4wm2InOpjNJ0uPKNn+kRxC\nCCGEEEIIIYQQjkx+BAohhBBCCCGEEELYA86rHcwkSyYtNAmkyfwoE3v1q189pynz5r0oGeM1PFn8\n2muvndOMJHLddddtvC9ldS9/+cvnNGXUlIRTEs8yUJJGCR8l9ybbowz++uuvn9OU3VWdLc8j/D4l\ng4yEQlvLTTfdtLF8FgmGMnU+s0mZWRdmy7HT5s1awnZiOSl5NMmynYBvMuhdoBNpiHVv1i3KFe+8\n8845/e53v3tOs/5sjJuEme3Aa/hdlpl9xyL5UEZr0kv2TfZZk4jecccdc/p973vfnDbpPvOscssJ\nx6wa2ywAACAASURBVI5FWzCrKu/BslpkQNYRZfp8ZkZK4r04h7CdeA37GecTthntOpyLmT/tNiZx\n32U68l62m1kz2OZmReT1JkE3KwOl7yajtnXfZPC8huOUaYsmx2uYp60V5LCoVp2omp2Ik8zX1m+z\no3PuMEu1zbMcy2Z7sUgwZKlN1+yZHbvFLmD7WNvXmHWAfaoTVaZjvyMWoaxjFyYdmxvhs7Dvsz9y\nDuGYNVvNuq2Qfcaix9paaREnl9rBiFmViY0jjn3mw89tvuZ7hVniLVqusWv2arMwW5RJi2jZeVb2\nF75zsX1sz2zzg1mjeC/rs2Yn4l6Ja4XZ0zpHZVgU0dOnTxfh+wDb49Zbb53TtF4zbfXCurMoWsTe\nMax/8HksGlcnMrFFnrN9Qifitv2ekuhgIYQQQgghhBBCCOHzyI9AIYQQQgghhBBCCHvAzvhXOhFP\nKL+iRYmSP0YpotTLInZdeumlc5rWBIvkRXkaLWOU4Z06dWpOU7pF+bZJuSmLpZ3CZMN2cntV7yRz\nlo/yQcrneD2lqiZ7Y71TkteJTtKR4Jr036xHdrp75167Jlk3WAeLT5dHO9PWY1GxOiffm1XEMGkk\n++y999678buMDEA4vijnZP81yyMtTWZbI+vzm0XWM1ks5wXOWSYp7/Rzs66Rjr3H2o/3tchiHZmy\nldmsKEv79y5g1gzOf1wHOS46/dNkyGwT9kFG1+J8zDWa44tlNrk382eaazfLY+uARZ4kFkGp6mwJ\nvkn8OzJyW5uYP+uRaX7Xor9Q1s78+TwWfapjA7Y0y0+sHnaZozyHzet2TUfm37ne5kibyy1Pm+87\nUWFt7WK/4/zANY2fc1+9jtnBzH5jkYM60b4In7kTBc/mbouGyc/NDmZRkDk/sM3sHcbeEXZhDbX6\n4NEc7G+dyMA23jmnHjt2bGM+nSicvJ5zPNNcm8wOZhZpWx/MImoWcpsHmD/ftavO3n9zf8zvv+IV\nr5jTnchnNn5ZL7Smc+zweIhOBC6zOZvti+nD7OWb7mt9hZwrS2aUQCGEEEIIIYQQQgh7QH4ECiGE\nEEIIIYQQQtgDdsYO1olQQGkkJWC0e5g0krJSSrEoSaMcm9fQukWJFk+bpwTR7Fa0zDBPStiuuOKK\nOU2JnEW7OkyubFJg+85S6bPJhU0i2YkIZf2AUkWzp/G7vL6TNjtJR5K3C9J3k6R2LD6sb/Zb9nmL\nRsW27UQb6UjQ+V3Kfd///vfPaUphOSfQ8snxy7nFIpRZ1D+z51Cyu96vzbrFuqNEnnXB+Y520E4k\nBYt+YuPObJVmeyFmC7VIUaQzR7HMNufswtjsRNVku1lkG7NHcIzYuDMLJO/FtYlrJfM0mbbZldh3\naBvh2KTt1CwdZk22yKEcZxxDVWevxx1pt8m8LVIeo6JwPmJ98XqzlPPZWO98HtY1y8bvsu3Z3ha5\njPsSW9Otz3WiKW0TZpfh5xYd0ayXXDuY7uzrzL5hlk/L0+xQnfmH92J/5JxgUbDMGsb+ZTaZKp/z\nWW6Lpmd7CMuf8JmZv/UDwmewurAjHjhP8X2G97JjHIjZ/8y21IkmdqFh2fnc3KdZJE1bFwjnQka4\n4vrAPTD3mbZX6tjQLBIo24T3feSRR+b0Y489Nqe5hnJtsci01tc4xu1Ylaqz37HZP/k5bWJ8Zlsj\neG/myT3wG97whjltaxDbhtfw+e0IFPYD21t0rLy8nnlyP8C24TrL/mRj3Nj+VTaEEEIIIYQQQggh\nHJn8CBRCCCGEEEIIIYSwB2yFHcysQibJNIkiJdKU0VJKyuspjaMMjRIwyrGvvvrqOU3ZJmVclHZS\nbkfZGr9L6RbLQ2kb5WCU7fFz3peSscOksybnNdmnSWdNUklJm1l3LGoYP2f+ZgdifVHmZ7Yftgdl\ni3aqvsmvre+SXbCcWJub7cs+t1Pt2RcolzX5OtvcIlGYZcok2LSNmHyblkxeY9FM2N9NIm1Rxqyv\nVXlfYr1YpEDOU+tWljNYxAGT3R5lLJjNgPVrElY+l9kniNkBLOLDLsjaO5iVyaTsnCPZDhynJkG3\nKCFcmyhVZj5ci2kfYzuzz3K8cD299tpr5/QHPvCBjc9i+wc+O8e79c31fseyrkcOO4NFLbEoaKwv\nPj/nSto/zWJpayvzNxsAZflmjbHys11Zv7QlWPSdzhq6C9gaZG1iz8o6M5ug2QEJ9z7WnzvRnzqR\neW3PxT5llguzJNr6azbPw8pKuO6a9Y7Pw/uZXdHs1RZ1yCzPZgeziFDWlmZJs75i9iSrz07UswuN\nrVnWtnzWTsRM9klGgmb9WcSubpTYM3T2NXxG7qc47lgGlplrMctgVkV7d2aac1SVr+sWvczmJovI\nyzbj2Ln55pvnNN/77rzzzjlte3HbD9l+y95JOxEW7Z3a3q/YNtwnLLVRRwkUQgghhBBCCCGEsAfk\nR6AQQgghhBBCCCGEPWDr7GBm/SAmp6J0qxOBipI2ytAou6W1gmney6L/UP5GObZZ0phmPVDqxQgL\nLKfZuViedZmYWe9MGkrJnFnyLNoTMUl0xxpEGaW1KyWJLA+l/5QRUh5qkVw6lq6lktptwsagjcfO\n56x7Whko1bSIBp2oUxa9zfoj70WZ+kMPPbTxc8pZGW2AFlGLGsaxb5ZVi55W5fJf3o91ymtYv2YT\nJRYBwuT+HGtmeyHWV5gn51+rr6OMI9aPSfR3GdaxSdzNbmsyZNKZC03uzXZmf+T6ZXYo5kPrEscg\nr7conBbF79FHH53TrEPOA+vWB679JttmGzDN57EoaMyfaRsLLIOtldxDcH7g50vHNZ/FbOedyDe7\njNmfzQ5m0UfZF9j3+Dn7fMf+THuTWVEsOpjZ9YhZqmm54J6W6xXLY1YnYmvU+tjsvD9w/NMqY2uc\n7WMt2qZZsYjNG0xzbFokQV5vfcLeTywip83vVg/bitn4Ota9Tp6dvZW1rUWxXFrHnSipFv2VfdCs\nvba22DEhrGdazKrOXu9tfWWfZLoTDZF1ymfgHGrWbGLX2JrOZ2aZOxHNrX45F9nvHZbuHJtAtv/t\nNIQQQgghhBBCCCEcmfwIFEIIIYQQQgghhLAHbIUdrCM97dhxTFZnUkfKVilJ46npjEJCOa5JsykL\nNPkYZV+Uy1KablJuSuooeTNpm9lqutexXuzkdpM8Gp0oGmwni67EMrPeH3744Y33pU2M11u9dKIq\nmG1nl1lquzFLH/sCrRxMs09RImoRYzrRLWgPYT/iNWz/D37wg3OacwJtE2bdsv5oY5Zlu+eeezbe\nt8rtrKxTWkN5vc1NFtHNZMqU+VpkF5O1m1SV84lF6GM90iZk1j7CurKoPJTa7kLkvg4dabNJ3zt1\n0JnbTIbciURnFium2a9N7s22ZX9nX6MtmOsvy2lRAg8rn1mxOEdw/LPcNt6JrVOsO44R3pfzkdnt\nbP9k6wHvxWe3iCrEok+Z9H1bYX/r2BrMQkTYVy3irVk77b5mQ7P2MQsJ24TftchaXEMsAqRZwGwv\nTdb7iFmf2P9ZDqsvps22aesR82dbso4syg/HDuH4ssiAHRvaUruRRdjaBSwiMZ/DIjiZNdYsVHYM\nhr0DdvZf9t7asUZx3HF/a3ORHRvCMphVnNdwv3bixImzruO+/6677prTHCPWHp19jO0V7GgGq9/O\nex/va/Oyzb+d42zMCmd7ps66Ymz/KhtCCCGEEEIIIYQQjkx+BAohhBBCCCGEEELYA/IjUAghhBBC\nCCGEEMIesBVnAhkWstI8cryGfs2Ov51+zRtvvHFO83wgC9lIDyH9w+a3t3Dm9BbyXgxNf9VVV228\nl3kdu2cC2d/M42i+RjuLyeicGcB65Pkq/Jw+yPvvv39OW1hw0gmpZ2ecdPyju0AnlG/nPB5ew/7J\n83UYKpZn7TzwwAMb87RyWh/hGCHsy3aGAdMPPvjgxjS9zvQhszw858DO8WL98Mydw7Dw6TYerX9a\nSEnWL+cmnp1i5w/Ro2zzjp0zxHpnndJjbmeHmPff1gwLjbvLsL7tPIjOOUA2vmz+Yx2zPTmu+Tnb\nv3O2gZ2dwOe1kOp2/h/Lwz7OsvF8vvUzgayfG7Z+sZ/zHnaOis1ZLA/PCePYsbOI2JZ23omde8T2\n4L3YzzrneO0ydr6OnW9oYeRZl+yrXC9s/NrcZusa+4udFWT92tZ65s9+yjTHHctpZ2nxc9u3r59T\nwvKxn7P/2557fZyfobMfsjNn7Jwem2ftvB8+C+mc/WP1a+9Rdv7Krp2fZ/ugTlvxudnOrCe2CfO0\ns2aI7U3sXoa997Ev80ygznlg9k5t9+Ucxb33TTfddNZ3eCYQz9zjGLT13s53tDOLOI7sLFDLx/Y3\nTNt3ie1FO2Vmndrcbes167NDlEAhhBBCCCGEEEIIe0B+BAohhBBCCCGEEELYA7bCDmbyq45FhfB6\nyr5MUk0pFmXax44dm9NXXHHFnKYUy+RgFs6cUJ5H6TelmpSD0Q52zTXXbCw/6YQlrPLQhybDMwmy\nWT/serYlZWyWD2WXFm6b7c3wg5Qg09LCfmDWGAv7aFLejvRzWzHpoqWtbdmPOHaYZt+m7YLf7YSF\n5/W0QVx//fVz2iyZtAY+8sgjc/rhhx+e07S0nD59ek5TTm52KJaHElmWx8LBVvXCI7N/dkKjm/yb\nn3OupD2P9cKy0drHdmXbMH+zDfBz1hHr1+Y7o2N52gWs3YhJkk3WTjgubF3jNSZBZ39h32bbsn8x\nH/aFj370o3OafcGsGzYfc21lnsyH1hteb2Frq1yyTlgm9uGXvexlc5rPz3WKcxPXLM6VvMbmQa6P\nHKe0bvG7vJ75mEydn7P8fHau3exb7BPWfruA2Qhs78DrmWZ/szqwfmcWEgsZTdiPeL3NCWanIHwW\nrq3sU/fdd9/GfNgH2ac4hsj6Osm87NgFlo/zAjGrF/cEzIfQ+v7yl798TtvREmZPMuugHXXBsXnl\nlVduLA+vYfk5BlmnnCs4by4NQ30hsD2UWcNs39QJ6c12ezqwdxSzaduei9h7YsdKxc/NIsfxXnX2\n2sf+z30Dsedhe5idyvaKtm8wy7PVr1nTO/Z1s9qazdzWEl5ve7UOUQKFEEIIIYQQQggh7AH5ESiE\nEEIIIYQQQghhD9gKO1gHk6LZNZShUnpqkRcsApedum0SVko1KT2jDJ4Sb0q3aJmhdM6iC3VObj/s\nhHmT9JkMzySjndPRLUKGyd4IP6esnRJh2lUYHYx9gnJ66wd2+ro9b8dasgt2sI4N066x6Hjsz7RY\nmoS5E7XBrIRmB2N/sahk73//+zeWgXMIoxlQQn/ttddufBZGQqA026KfrI8hG7ccO2YzMPm+RSXg\nM5sdjGOHY4GyXrNu0YrAsvFzzpWc7zgeOxY5w+T9Fzsmdzd5skVO41izNrF51+re5leLXkVLBy3V\n7LO8F9dru94icVmfrXLpuGFREgltKXxOrmsca5yPuF/hPMj5iPfl+GLZOKdz/rIoJNzrmIWzE5l1\nl7H2Z72azZnjjuuRzf2sY2sTiyJlkRjZv9jPLXIdx4hdz2fhnGB7V17DfmrWksOOiWD5OKZOnTo1\np/n8nBdsrjRbGfNnG3BP8AVf8AVz+pZbbpnT3IuwDBzXnIPsXcXWYl7DtEVJ47PwGqbZL/ndbaVj\nJ+rs5e29x44x6dCZFzvRXO279rwWqdKOMbG5yPbqVuYqf8+yIwLMrmV2MOurZu3j5/bObOmOTYxp\nO/aEdcdnMRu1WX9tXelw8a3KIYQQQgghhBBCCOHzyI9AIYQQQgghhBBCCHvA1tnBKK0ySSblTpRB\nUVpFy8aDDz44pymxZKStG264YU5TXk1JGm1c/JzSNpaTdhJ+lxJcyr4oI6WEk/I8s2eZtYscZoPo\nWEv4fZPDkY5E0k415+esX5Oys5ysa36XFjD2FdajSSFNwrcLVq+lLI3QZ9GL2FaUc69H2zkDpaBW\nxyZ7pBSU0NbAMjDqBSXYtD1x3qBcmtJ6QrkrrXC8r1khz6VVwiSvZj+z6GAcRxw7HHes905EHJMR\nm+TaLEMdyyJZGmly1zBpsNkqzVrSmdusHWi5sChgxO5rkYA4TpnmeGQ9ME/aFyzKWCeSXtXZddex\nXpsEn3B+YZp7CI47Pg/3DSbr70TZM2k911CzeBPWyWG2uosBi8bFuX1p9DPbp3XGUWfeZZ+3Odjs\n3kyzv9gemOsGx6xZ0rhe33zzzRvztPKsX8e+xznC5i9in3eiRnGM2/EFbBvawTjeOcZtzFrbW0Q/\nziG2ZljEJTuuYVsx+07HLnMUe5e9n1pE5Y41zMrP73JMsb9zHHBN4B6Y/cIiA5rFyvZu69ZU3pv1\nwvJxbTbrOD/nvGPRuDkH2X05HnkN5xMbI3b8ArH2s/mUdWcRzdge9vtIhyiBQgghhBBCCCGEEPaA\n/AgUQgghhBBCCCGEsAdshR3MJE5LZbGUZVGOTmkcJV08Tf+6666b05RwUqpK2SalZ3Zf2kks0tCJ\nEyfmNC1gLI9FPFkqNVync0o+P7f00lPyTRpHOZzJ7FnvFkWG/cYk6CadZRnMrmOSbpMU7gJ8bkoa\nKTNk2mTnrEvaLWkvoDyTElarY5ORMm3Rb6y/UEbKyGW0QN19991zmhFPaBnjeGf+fHbWLcc+y7Au\n4WS9WHQH9nOTtlpUAsIxcu+9987p06dPbywDrW60oliUKcL5i3Vq7U3JstmczMZgkdFY77tmUelE\nKjF5sj3rUWx2ZsM1LBoG+y8jYxLaI5jmd9m2tlZyP2BRK822WXV2/7Q1wiyNtITwGtrXWT7WKb/L\na1gGls0k65yDbC228WvSetY15y4+F+dKs7iznB3r1LZiezNiFhLb99oYtLXY4PVmM7Iy8Lsca7Z3\nI+yzbGezrvCIBrNhHWYHsz2xRdjp2PZsf2j1aPY0wrmM4532Wisb28As8TYX2VpCrN53gc6RFbbn\ntHTHbt6hY6W192J7FrM/WyRk7pPt/c/2AHaMB9co9s2qs8cqLc9cO6zNrEwcd7w38+Q7POuI65Gt\ny3YECj/nesf8WS+sd6btuIPOu5Dt+ZaO0yiBQgghhBBCCCGEEPaA/AgUQgghhBBCCCGEsAecVzuY\nWYiISTiNdcnZGSh5ttPRKZ9k2ShhvvPOO+f0Aw88MKdvuummjflYpB1aw2gbofSddjDaSWg/ocyP\nsjKzpx0miTQ5qNnJ7HNK8uzEcovIYpI5k+kyH5Pssz1Mus82MIkd+4TZT0w2elgktm3EZJ98Dosm\nQSxiiNmSWN/Wj0yazXbo2PgIy8aIJByP7Ee0glpftsg8Znc4jE7kP5MLW5ptxrnJIily3mGd0npH\n62wneojJaE3yapGJrB55jfVRG7O7Bp+V7Wlzs9Ur+xH7M6/nuKC1mdh8yTzZ10zizbWb15ilmnYS\n3pfWRloYLeoI11aWeX0Osb+Z9c6idNl3uV9hmve1SEu0uHP+os3k5MmTc9rmesr1LYoM25uSe4vG\nymfszBW7NjbNLtCxmdizWj5WfxYdz9ZxWx86e4Cl7dOxUDB/s3Na9Noqt69blCPSeZ6lEdrMhsbn\nN6s5xynriOOR87Idm2DHHdh7gkXwJLu2v10afbVzfcdKbFG0OnmaBczWWfYpW0PYd/i+Ze/RZp00\nGyL3ISxP1dlrCsvEfM0qZfZke7976KGH5vR99923sXxMM4KvRUqz/aRFCTQb+FJLns0t9s6+1JoY\nJVAIIYQQQgghhBDCHpAfgUIIIYQQQgghhBD2gK2IDmbRpcx+RJkZP7cIGBaVgvlTnsaoOHfcccec\npsyZMkzmQwkrpe+UY5skj5Ixe17myetp0TDp3Dom6eTzUPZnES8s0hZh/ZpUnnVB+PyU/pssn/Ye\n1gvliHaqvFkQO3Ywszfsgqx96Ynydr3JEk0i3YletdR+14l6x/7C8UhLJqWalGxbNKrzEW2jEzHR\n5P78Lucp2lwpo+V4oT2V44v2E4vWZ1EMWAa2E/MxewOxNrBrdhmzaXBeZJvY/MrPTUbOa66//vo5\n3bF0cbywbPyc8zfLTFsW8+eazvWU6zuvv+eeezY+i8m0zcK2PuZoDWXfZj2yn3fWF+Zj92b+3Isw\n+gvtmZzXaNcyew/tdsyTcyIjNrFOmf973/veJ71Xxy611MJxoelYdYntC9h3LDqN3YvY/sjK04kG\naHv1znc7RwhYxECzRB8WHcysOIdFzH0yOtGSiN2LY5lpzn2c7/gsXJc5t3asg51nZz5M7/IaarYm\nq4+l+wirJ4vU2dlDmlXT7sX8LRIf3+cskhfXKIt2xe9a1EquCVVn7/d4HfunWY+tXng9+7xZz/g5\nn+eqq66a09yLcD5at7dtyodlpsWMvxcwTZu62fw6x1scZWzu1iobQgghhBBCCCGEEJ4S+REohBBC\nCCGEEEIIYQ/YCjuYQQmcRYOhbMpO/qb0jLI3StMpw6Tc22TnlJ3yu7RTnDp1ak5ThkZJl9nHKPm7\n//77N96LUjKLqnZYZKKlErKOVNtOR+d3zW7GNCV/ZiUzewjl66wj1p1FrupE3TBLT0cquguYzNme\n1aw/nfRSq5yVrZMPy2wRgmh1otTU5LjsgxYlwMZK99k7Ua5sfjRJLccU5xdaYXk9bSaUs9JixOc0\nC5/Nd4TRJkwSbTakpRaFXbCcWOSWo2BWSpOac4xw7rTIPlxzSSd6m2H9y8adRQLlOm4y+MPojFuL\nwtOJFmPjlPsS7l0oU+dzss14PfPkM3Me4H7F1lzWu0WjMRtPZ5wexbZzvuhEaCRWB7a/6Ix3mx+s\nf9n1nfnVym+RWjkGzZLJMWhRbu3IgfVn5L6cfdX6oWFzk9WpjWuud6xri2LGscN5qmMXtXnQ7PeG\nRYg0u9m2wnnR3o+Y5pxNrL4770PWPtZWthazHdhHOHZYHkaZYz3wfYh2JYv6xzKwfqyueD0ty1Vn\n7y15BAGfgftJi/jLNPfoZt2izZltecstt8zp17/+9XOa9WJrt80PXH+PHz++8Xpaz9geLLPZ1mi9\nY3n43aV7xO3fAYcQQgghhBBCCCGEI5MfgUIIIYQQQgghhBD2gK2wg5m1pCMXNzuY2SMoi77zzjvn\nNG1flGhRSsY8TTJnEUxMjkjZPK1krIe77rprTvMZb7755o1lMyk6panrf+vYeyiT4wnqfB6Wj7JF\nuy/rkXkyqhfLTZkjP6dMjjI/ygspQexIk03ieRT7yS5gUt+OpNqe26TmHL82Zi0fk9SafN2iY1l0\nEvYps8WZXLRjeTtMXm1RzUgnOolJ0M3CynphdLQrr7xyTnMeMIuOtSXnO97LorWxPTr5Lx2DXQvQ\nNmLWJ9bTYVGuNmHRqDiXc8x27F1m27Q+azJwztMmR2c9XHvttXOaFmE+l0X2OMwmyO+YrZTrjlml\nbP3l51xPCZ+hY3VkOVkGs13zvhyn1p94L9Yj8+ysDZ15b1theW3eWprPUs6VXdSs9sTGKdu/s8+y\nSGE23g+zc9l89HTP852xbDY/SxOLINUZ++cq4p6N923lKJb8Th3b9R0bptHZ09nnnLNpHeb8bRG3\nO2UwO5jNdXzXrvK9H9dpi1jGz1mP/Jz7Ve4huVZyTuH780033bTxu2bbpF2L760sD49N4HzK/Hm9\nrR+2B2J57P2kw/aP5BBCCCGEEEIIIYRwZPIjUAghhBBCCCGEEMIesHVa+I4dzKR3Fh3MJMmUiT38\n8MNzmlFrGDGDEm+LEEQZHm0WdlI9LUom0z558mRtguW0KBV2gvh6uU0CaDJvSvUsT6Z5b8rYKHXj\nyeqU2FFWaNHaaFHhqews52233TanTXZI+fLSqDxHkeRdaMymwb5kkmobs2YBswgmds3SurT+a9Yo\nPhevt2gvJuu1aCYdCe5h/cueh/ejTNTGP+cm2lz5OctKCxglu5Tdmi3W+sETTzwxpykP5njkGCRW\nR0eJMrULUU469pCltjx7blsTrI47UZA6tlqLJmeSfotmQrhe017MNaQzp63nb3ZQs+cZ1k5sA46R\njlXKpOOd8lh72B6Cc6jN4yxPxxa4a2toZz63iFodi5JFpTQ6EUptXTO7kuXJ/kWLA6/nno59kM9u\n66YdOXCY9cbmrI49r1N3Ni907ED2uY0jo3NNZ/41q3xn37sLdrCO1cuOJrCjBqwfdcaIRdK0Md6J\nwMw8+Z5Eu5Idj2B92eYKs4bxvZD34pEDVWcflcLrGHnWLNkW1Y7zC6NuHTt2bE5zbmI+fE+0Iw44\nNrkWd/YA3NOyDNyX2LuHvYdaexzFRr39IzmEEEIIIYQQQgghHJn8CBRCCCGEEEIIIYSwB2ydHWwp\nFv2KUjSmKbPid2lToByONjFaIiyKCq+n1YuyMsrBKE0/ffr0nKaUjHYNSmQpBaRkzKKXrNvBOvJf\nk6IRk7OyfJQC8hqTyVFuR5uc2epYNuZp0SbYD1inLENHmn6xRAQz20UnqoJZUTqRgM6VHWdpPh3b\nl5Xf7F2dCB4dmfb69zswX9obOSdyjqMdjHMQn5N2MEZMpATXxgjLTxktZcocy5TjmnXlKLZAYtaC\nbcXmOZOsm5XHrCg2ry+NKrM08kjHYtZZfzqyec7rXAe47pmse/2+rF+uTdZOnbrr2OBtXma9Wxt0\n7NudObRjB7O5cpfXR+PpWNfYP5mPWe2NzrimrcNsKTavc0wx+h7XHK4VtBEzH4veY/s4s42vY+Pu\nXM35FhnQbH6d/t8Zp0ZnnunYaG0Pb+8F24q93yyN7tvZBy61M5POsScWUdmON+EY5H6KY5Cfmx3M\nyslnMcsn3/mqzrar8TvcZ9IORrjO2rsb38+51+V3OafceOONG/Nkn2c+PH6E44j7W5af9cj8Oca5\nhhLb55ltnG3fibJMtn8khxBCCCGEEEIIIYQjkx+BQgghhBBCCCGEEPaArbODLY2EQqkUJXAmgzeJ\nHaVehJIuSmcp16LcjNYt2tAo3aJkjHIzSvIoH2P5LZIVPze5OtPrdGSflNLx3mbDYx3R6kYYlern\nJAAAIABJREFUtYX1YtHdOtGq2GYWxY3yXZbB7GCdfnmUE9ovNEvtUZ2IWsTabanlxFgqv+dYZnko\nz+zYMsyKYdL6o9rfOhFMTA7KMUULGGW7lOm+7GUvm9O0a3GcUoZqkmLKZSkJNjm9SWHJuYoYtwvR\nwUjH4rA0yo+Nd4sSwrFjMm2Tylv0E17P/Lmu8XOzv5nsn2WzSCOsN7vver62Tpmk3j7nPWxd57jr\nPA+/a1EFWWbOfVwreb1d04l6ZW3zdFuFzxedcWRzpPVtsy7anojX2NpkadtXm9WFfZB2MM7xl19+\n+ZymdYN5sk+xHrjftGhi60ccMK/Ofmzp3sXqxcZjJwKcWeCY51GimbIerGwWJc72Q7tgB7Pysg5s\nL9CJlmzYWO4cj2B7SIssZnMC25DvNBaJi9j+0dZujmvuE9ftYPwbrVhMc/9p79gWfZD533LLLRuf\ngdd0LFe2r2Z78J3XLGCdd3Kz5tq7Occm8+H7eIftH8khhBBCCCGEEEII4cjkR6AQQgghhBBCCCGE\nPWDr7GAdOpI5yu0o+6KEymxMjMxl8lRKCjundDNNewTtYITyLpOUsgxM8158rnU7GJ+N9cK0nQLP\n+1ES/+ijj24sK8vB8lEyx/S6BH9T2cxiZLJ8swvyvnxGsxyQTgSxXWCpBawTRcvkuEujJxwWAWTJ\nvWxOIOxfFi2IsE4s8mAn6spRYb5m2aBVlXYwjpdrrrlmTtMORhmx2YHMlmNSVbNDmGXC6qvT/3bN\nWkKs7Euj33SiaHUsFDZ2rAxLrbRmcehEHzP7G/uXWQ/5ua0DVWePKUa747puaz/Hpll3eG+mzQ5m\nbWYRylgeiwRkdjCzCiyNgmNWQ4uCs8t0LJakY6czOxjb0/o5sfnS2oqwD3LPzPbnGsIoQIRtznHK\nvk+4hnD8Vfnez/aNS+dKY6kFzCIX8mgCpolFY7LjKojZWy7G/a2tI6xvOxbA9redNXRp9NGjRLEz\n2z37O+1aNk5t7Ns1rB9arCyC9jqvetWr5vSJEyfmNKOXsR75Xs33TVqeOQZpT+UY4RrKecSONTAb\nPK/hnMPPba3k2mr2McJ+wDIzzTrpRBIkUQKFEEIIIYQQQggh7AH5ESiEEEIIIYQQQghhD9hqO5jJ\nD02qZ9JmixZlcjWzYlFuRrkdoyHwviazp2zPLAuUklnkFJOCUhr2yCOPbHyuw57H5G1WPj4PLSeU\n6rFeLEoZZa6UBfLZPvKRj8xpSvgoc6RMjnY7tpNZfezE/KOwC5HCzA7WiZBlY/BcyYefbluARRvh\nmLDoSzY2WeZORJ2ngsl2WT6Ol4ceemhOM3IDy0rbl0UqMXuDSc3NakrMemn9ySTaHQvYLsvaO9dY\nNJ+lFoejzFsdeTnpWGMsbfe1fDjHs4/TfkGJ+3qZuX5x7TNJto0FzjUWvcYirjHdiexpY+QomAVi\nqR1sl+lYvVgfHTuYrcXWj5baAW0NsvnSogJxveN9zd501VVXzWk7ToF5cpzyvjxagOOv6uyxSasY\nx6ZFKSMdS0/HtmyWK+6NudeljeWGG26Y09wbX3HFFRu/21mjbf21PU0ngtK2YvXNdwWz83csfUfZ\nl9rYtHdYa9tOf6dFy8adWSTtWA7WJ8c4+wjX1vV7Hz9+fE6zD3Pu4DOb1ZrPwDZjPkxbJELCfDhX\n2Ppr+35bl0knQrkdN2Pv7Otz4pMRJVAIIYQQQgghhBDCHpAfgUIIIYQQQgghhBD2gK22g5llx059\nN5kc05SoUYZGuSVlWSb7okSL1gpKsSyyCb9LiZxJtimLY5lNqkY7FKN1rduwmC/LZFJjykpZR3wG\nShJZPsrYCKV6Fn2MabMZ8Bq2B6XDtMYQykNNdmknrlt5dg2ONfYT9jeTnVsECZP88xqLzsBr2J85\nRihJJWZhtEhnfJZTp07NafYj9gve12TUJgNnXzvMetiR79v3OdZOnjy5Mc2xwHF66623zmnK0Tkn\n0GLJ8W5RN2gR5Xd5De03rKNOhDZi7U069qRtomPl6UTjMtm5rZVmHejYTCw6nkUv4viyNdGi33Dc\nWftb1CGzq9AWud6PLMKo2SVsXuhECmO9cHzxXmYxI1aPvC/r3eYp23tZ25g9jZglqROFbtewowws\nUqLVgVk5bH20/SfLwPbvlN+OXLCxaZHu2Gctih+xaJNVbpHgdVzvDI5T23/afW1OIHx+RlDjfWnj\nYd3RMsbvWlRji5ho0YjM6rVr+1s+B8eCRZSyvT+xMWvrbye6pe2rbW7u7JN5je2tDIsCRlge5s86\nXI9ux3rne3hnLSf2zsA078U50Y5mILyvjSlrM85l9v7D+7LNLLIhP2eftuMX1ufEJ2P7d8AhhBBC\nCCGEEEII4cjkR6AQQgghhBBCCCGEPWDr7GAmAaPkyuTSJv+j9IySakoyzR5Cqdvll1++MU9K4ygZ\n470oAeucGk74LJSF8nOLIkErxvqp4axrythMUk95H+ud1rAHH3xw43cZmYt1zTple1DSttROQwvY\nY4899qRloDzYpNWGyUB3ISIYMTtgJ1KJRSAyG4tFH2Cfskh0Vh6TqZu83OSWp0+fntPsRxa1wKwM\nZhM0if661LpjvWNeHNvs5/fee++cZqRA1hHl5VdeeeXGMtFWynuxr5gcl9Yz1jXbg2OQY7wzBs3e\nZf3PLC27jEmPTWpN2Ca06/FzsxlZFBob72ZL6kQBs/G+FI5frj98Rq7v62PT1nJbN82GZ2scx4VF\nMjO7h0nKO7YssxKxDGZL6NjmbU1kOZ/uSJBPJ50+aRYn29/aOLJ6NTsJsbWl812bU81aYfYFs5yY\nPY3f5bhZt6xaRE97HpsTO3Z3iwRk66NZ+Lj2sZzcf3DsX3PNNXParD5mNe2sE5022wXs2A2zKJr1\nx9Y4i/pnEass+pj1tc5caDZSRpA7duzYnGZ/sb239VPCz21dWo8OZhEkzTreeQ+xqJrEotPaGLE5\n1z63qGw27rjP4FzGNK8xmz05yvtmlEAhhBBCCCGEEEIIe0B+BAohhBBCCCGEEELYA86rHawjJ+xE\nNqG0ipIuWjaOHz8+pym3NMsJpViUz9ECddNNN238nNItK4PJVCkfs2uuu+66OX399ddvvBclqLRf\nMJ91+5TZciyaD8vx2te+dk7ffvvtG9OMtMT6vfnmm+f0iRMn5jTbg/dl2zNiAqX8999//5y209Qp\nF2TkI8prKTW0k953zeq1FJOLm9XGvmvySas/k56ancJO2Wff4dhn+3O8PPTQQ3OaEmwbHxZdwua3\nTvSHw6S/Zjfl9/k8991335ymHZL1YnMN7Zycm0wiblJmzjXrUQk3PYvZlvjsZr0hnc932XLSsXIQ\ns+mYFZj939ZZ0rER2BxiESBtnrFriFlULFoX5fEsM9fW9bo1Sbb1VYscZHYF7i245tKuQKm9RT8x\nuTvLb1EMCdvY5iJry6VWr12I1kcsEpphdiI+t9kzmbZ1h7bdhx9+eE6znc1+RLuw9S/COd7stuzv\ntE1YtE2OTRs3to5VeTRJ2n5sT0hYPs6PFrWX5Wadci/N9uBYZpp7Wu5RuRYzIpgdicB3ALOAsb2N\nji1wW+HYtKhYnX5u49rWX4uQa1Yyw2xoZgXls9AOxndGvut0jkqwvbfZKM3ivX4P2xPaez7rwo5a\nsPdqe0/g/Gvrpq3jTHOMc46zOZHzCecys8JZtC+zyi99P93+kRxCCCGEEEIIIYQQjkx+BAohhBBC\nCCGEEELYA86rHczkwB3JIaVPlHoxEg6lbq95zWvmtElhKUmjnYLlvOSSS+Y0pdkmbaO8/NZbb91Y\nBkq3TPpLyRhtZcyTz0IZsEU1oiyw6mwZGyXotMdQkspr3vCGN2zM533ve9+cprzt1a9+9Zx+4xvf\nOKdpsTNJG2W3fAaWhza097///XOasshXvepVc/p1r3vdnGa7ss+xn3XYJ5tYh45E1urMLH283mws\n7L82rnnNyZMnN35u9hCmKWUnJqm1OlyXB1sUJZs7KB/lPEJZONOcNzl33HbbbXOaz2bSZ15j8yzv\naxYYytrtea0PcW1YagfbBVm7SaSJSY9Nts3rKTfmnGfRb6wMxMZsx7ZmFrCOFc6sTjYe2e8sAuBh\nsEwWMdOi4xHaVWh55ti0qDbWxp19lcn32T9opTArrLWf2auJjdldiEa0dE3sjB2OR65rnLPZJqzX\nxx9/fE5zX2bR57hu0B5kVmiOKX7ONOFaxPJbVCb2FzuW4DA7mPUZjnNrAz4zy0GLBzE7BuEzcCxb\nlCaWk/tb238QztHsBxaxiGXjew7pHM+xrViEaM7TTJs9yvYddg3b4aqrrtqYP/dHnf2hlYF5Msor\n+ybf4Vhm21tZ5DKz/tt+ft02bTbvzv6Nz9mxbpl9ziziZlu1eceOYrD9E7HyW/8zyyc5ynEH2z+S\nQwghhBBCCCGEEMKRyY9AIYQQQgghhBBCCHvAebWDGXYC+VLJIeVtZt2itMosFPycsj1K+8y6REme\nRU/gd02KT9k4LVDM3yS4/JxyR1qjqs6W5DJfRijgMzAv1gulrZSV8pmvvPLKOc3oBrzeokSw/Vhm\nfpdloBzOIqrYfYmdTm+yyF2mY9kgHJudiCedyGKkE4GK13DsMDoW5fQcU4yCRTsYJdIsJ/uOWTHM\n/mYcdk1HYstnZhSw06dPz2k+j9nHKH+966675rTJlPk55wGTlDN/syVY5CrrW2Y/sXIujXK3rVif\n6YxfkwyzX9C6ZNFJrE1Msk5sXjdpuo0ps4yZxJvrO7E1jXmu25iYL8ttNgP2f85HHINcpxidlOWm\nlJ1jzaKcWDQ9tgGv4eeW7ow1S3essDZmtxV7po6Nxo4IILzGbAcWwcZspHYMgtn7LHIQsfw5Zs1a\nYZEhzQ7GOWp9XNsemmmLaMh6tKi9FpmJcwI/Z/2yDJwTuJ+wSHxmn2F5bB7nnGP2eHKxRPHjns32\n/mY35vzN9mRf4LuRvXO8/vWvn9NcX3h0Cdufa41F5DRbMG1fPGaDz96xddvewPYPHEO8ft2mzLnA\nLP9LLWfszxyDnQhfHIOsd9pWOXaYv1mwrV4s0jSf3aLZsa7sXcDmtw7bP5JDCCGEEEIIIYQQwpHJ\nj0AhhBBCCCGEEEIIe8BW2MGWQikWZXv8nHIqSv7MckQoH6PcjmnmQ3kXJWOUcVlEDspO+V3mSRuX\nSRBZHkYPMDtb1dmyMd6DUkVat1gm3o/5UvbG8lGqyDKZhJ5tZu136aWXbsyfEmFKIXm9SZ+JRUu5\n2FlqcTOrAelElSFm9zALJMcso3Aw/cADD8xpi9pBGTX7Pu2GR7GDWYSj9To3mTf7Ni1gtLTdfffd\nc5oRXywyEWWlrAvrB2wPwvmLad6X7Wpzt9mKzFrSsZmYdWUX7JwdizTrj32Ebcu1g2lKqjlGzJZl\n9+3Y0Dj3M23Sb0qz2ae4xlHeb5aTU6dObbye9WB2MErCq84eU7yO/ZlzBNf4e++9d07Tgk0LGKMC\nMTIo8+HcRFhfbFeu9ZxbKIm3aIhsV17DtZ79zCTxnfG7VMp+oTHLHemMX1sLzP7LdcrysWMH2OYW\nHY/XmP2IcF1mv+B32R9Zfps3uBYxT6576/XA5+R3WHcWWc0sIbSQMB+m+Wx2zASvYbpjjSPWBhYJ\nlPOVRZizMbjL+172Z/Zzi0bHduNzm/3Z7Ly33HLLnOZ8yTWB7yKE+1KbOzuWwaXRvngvi3S2lPW+\nY+sI4f04HrmHtPdtzlns/xynFumPdWpj1uZW7ie4h2D5zV5oZVhqke5EWDOiBAohhBBCCCGEEELY\nA/IjUAghhBBCCCGEEMIecF7tYEujPXRktBY5yr5rcmPKtSjtY9osLfyc11MOZ/YmytNoXaKki8/V\nsdJQBmmy8SqXr1PeZpJRWsBoxTJJGyOc8btWX4RSY5aN+VA6TPkfn4vX095jZehad86wa5FNzhVL\nI4VZH6HMc2nkGcpIKRF9/PHH57RJuU1qyvHI/mKWTJPOdiTV6zY3foeSVI55WncYEYzyb9bF/8/e\nvQddd54HeX9WfIwPkmXJn462ZDskBNNi0mlIIS4pDbSE0hIDPXBIOrQDMwy0HRj6B5QmJaVNoaUd\nmNIDpBAOSWkJpwRiOukQAp0mxJ3gGDs+SLYs2ZJlyZZsyXacxN79493aub6d93q/Z3+v7G9vres3\no5ml/a69js+znrXXd9/rJmvDlt5glaI4v1Va4Xd5beLxtRQzqwhFM9WILMT91NJPjF17eAxsDLJq\nGzyfVkXFqoNZf+eYY9UzraoP2x2Xw3bEEGyGdbOvELeffdCqiIxxdUoF9433AZxmH2QqC0P/Oa6x\nj1vaOc8H+xr3eb+q2XnLsRRROxacn/vFtmLrsgpHp5xyYikCZNdam5/Hw6qDse1wORzXiG3EKnax\nT3E5M/c1HB+tz1rFOWtHHMfYBjmmc3p/fyzVi32H28pjynQSrtvSUy01kv39scce201b6imPtaUt\nWSUybhuPC9fL1FYbH+0cn9q4aa+aYNuz+wJe/9h3+LlNsy/wuzyf3B67v+P4aPc4h1br47mdSWWd\nuTbbdX2/Hc2MF1bhzqr1cZls2/yupYXy+Np4an2B55v3tByvZ1INbRy030j2qoTL9McigZIkSZIk\nSVagh0BJkiRJkiQr8CVNB7Nw/hkWdsuwLIZ3WagU5+c8/NxC6Bm6ZdW+GObH5Vhop4Xa2jZbCCeX\nwxQopoNx28a4+njxbwxVJR6LW2+9dTfNVC+GxjF0jdXHuHxut4W5cjncTm7DXXfdtZtmqCHnZ9Uz\nvp2fIXwzFXFm0hRPjVUNODQclGGPXI6lF1ib5zJ5Phnayf5rqUgMkWa4t4Wps02xvVg6mFWRmDmG\nTGNhRaAxvJoAw90Z/sowV6tcYCG/7LN2TbSQX37XqnFw37hMXpus4pq1iZnUC0spnEntPAW2H1Y9\n01I1rSLNzLVtJt3DKvrZchgeb2lrlmJm1YjY921M53WDY8J+WhWvNVbBxcLuOT/TzJj6wnPDfs3l\n2P5YlS5rH3aerHIMt4fHhdd0Sz2ydMGZqjGnxq4rlh5nLB3Mrm1Mp7D0ZJ4rLpPnn23K0tPsvtdS\nBi293to4U/wtPZPbv78+Swfj2MRt5b0C+6PdT3BdnMfSM7k/vJ+wvsDXF/D42n7xmsB7A35u6YLP\nRzNju6VZ2f3FzO9Bmqm0xbZt6cX2qgRL+2I/5TJt7LLqUnYcDk1DG+PqFEX2C15H+B3r55Yubqns\nM5UoLS3c7j95r2u/VYyNxYemW868PmPG8+MXbJIkSZIkSS7UQ6AkSZIkSZIV+JKmg9GhlZRmQvgZ\nqsdUC0utOvRN6QzJ5DIZXsr1MgWM81i4PrdnJvXMwhcZRsi0J27P/ny2P8RtYprJHXfcsZtmWodV\nWjq0uhL3k+eYIbVXrlw5d35LS7E3ulv4+kxoqc1/CiknPFczKWAz4e4MjbSKITxXbCOcttQEu27Y\nW//tjf5MT2Rq47333rubZvuyyhEWImshogz33j+eTJ+0a8RMmhn7v7VPXhcshc/CTXmseewsvYvH\nmv2R1xO7PpiZFDALwT01Ng5aCjPbzkw1CavuYVUpyKqlWGU5spRHq8hJbC+cn9cchqLbWGfXpf3r\njJ2DmSqG/C4rmNgxYqqA3QfwWsb0EEvhtM8t1ZbXOKaT8Dzxc373MqmXp1Bh086bVR2aub5aSgXb\nv6VhcqyxtGtOWzqFpfRxfrY7fpcpU/yutS/2A37ONCZLPWQb3N8+G3ftGHG7WVHLqifyu5zmueT2\nsFIYjx1xf+x+m9vAY8dr3EylM7L7PxtDT6FvWmrkZVJO7XrGz5nGZOmQ9htr5v7W+jK/OzPWc3us\nb5KNSza93zfZp7i+Q9PbrP9yfhsfrUKq3dNburcd00P7hd3Dcdr23dZVOliSJEmSJEl+gR4CJUmS\nJEmSrMBRx8VbFaaZtCGGhc+8jdtCra0iCZfDdTEVg8tnyoWlN3F+e1v7TLgZv2vpafvz2bHjPs+k\nnFnlJM7P8DwLm7aQcs7P/eE28Jhyeyw9j8d3poIJzaROnZpDU984zWNv/c6qivAcMp3IwjAtrZIp\nR/ycy2S/YBWwN7zhDbtpVpzj/FwvQ1DtuNmxYnWs/QpK/BvTHhkOan2K+2mV/qxP8fOZylLc7rvv\nvns3beHO7IPcNksXtco31h9nwmtPLVXTzFT4Yhuxc04zx9WqatCh1cEsnN5C061SB9sX2wtTImx7\neO1imPlF6QNWactS8tiPmEJlIfFWrc/C1y2tfSaNnCwdjOeG01bhyapJ0aGvBjhlh+4f2yGPsfVl\nSz2zaUu/sLRuqzpk6WAzrC1bOshFfdNSwNjGLL2N271fSfdZlupj6Z9W7YiVwjg/r18cx5k+wzQb\n64+83nEbno/3q8bSo6wN08zrESxlx1KUuEyeT7Y1S8O032o2Vth22us3rB1ZJUF71YOlYI/h451d\nm+w+Y+b3qf1WnfndZ7htPC52v2rX+pkqyJbmx2nb5kP7eJFASZIkSZIkK9BDoCRJkiRJkhX4kqaD\nMZTJKrTMpN1YasJMKJa9xd/C3WnmrelWXcUqQcxUb+E0wwjJUuQsVG1/m2imsgm3ySptkaXu2LGe\nCYtk6CyrJ3C/uP9cr6XhzYRrW1u0t7KfWoi7hRNam5xJL7B5WHWLoaRMD7LKb1Z97vWvf/2587Ni\nBtsLq3QxBYxpWNavD02f4TYz5YvbM8bVoeBstwzVZV+79dZbd9PsF/yc+2ApG5baZ+eV81v/4nHk\nNnMf+bmFOM9co23brJ8eWknhRrPttXRmY5UoLb3LQqct5Yjn08K3bWxh2LillVmqKVMMLUydy2cb\ntG3Yvx7afrLNc5tYBY/zW+qLpbtYNROramaVffhdbudMH2eaCY8dUwUs/cTGFdvOU0jVnKk0ZPeo\nM6nnVsmL37U+xXmsypylIZKl0rD9MkVppkIXv8s2aPfnlhIxW+mJ67C0nKeeemo3bZUIOf6yzdvr\nJ6yyHtPBOD/vpTmeWiU+OwfcNrtHmfkNZmlIp9A3Z+5RD62Saey3EX9/8FxZuo9VMbMUTmtf7IPE\n73J+XtctrdAqT3KZbIOPP/74udswxtXHxY6v/Y6z+xXe97L/cpziNNM57fpo91szvwfsvNKh1eBs\nDL1MFc7TugNOkiRJkiTJdekhUJIkSZIkyQrcsOpgVvlrZv6Zt2LPpKWQhcUyTMyWYyF8ZOlNtl6r\nqjFT8YEuCve2iiEWWmahvZY2YtXBLOTbQnYtVI/rZToQWUUVSwGbCcueqbJzang8LNza2i1ZaoId\nM1bmYvgzQzs5D5fDPsUwT1b4YioGq39YFQ6rpsdt5vGZqSxnIfpMAbuoHXGfGeZtKSeWJmmpp9av\nua12rbDvcvk8vuyz+9UKn2UVNWaqClqFQWu7pxbWbtctS/eZ6afP1TXMqmNZasVMhTKbttQEqwZo\nod80Wy1kZvzi9Ey6tIWO27WGofn83FLSiMfO0u1m0umZTsDjy22eSV+c+fzU2PVmps3PpGXZtFXU\nsZRk2waysc/GYs7DdmH3opb+aOuli9oL12dtktvNbWXqOLeJ469VCJqp9GfjOD/n/lvFRF4HLKX0\nMq8jsPu2U7jvtVQ2SxOzeew+gsee7WimSjWvnXZueeyZlsXP2WbZdth+Lb2afYL7wvn5OccWu6e9\nKD3Txmwbj3jsOK7PnFe7D+CrCexeyq5Ndl5pZhyfefZxaJ+9TKrm82PETZIkSZIkyYV6CJQkSZIk\nSbICX9J0MAshm5nHQvIsTWwmFNqqd1m6A5dp22bbY2GEDCuzNBxbr1X1sfDw/TAx2w4LV5tJabO3\nxttxvMwb0XnOuJ/2JvYZM29fn0kHuyid4Bjx/DAEktPc15nUDHuLv7253ypTWciupZ9YRSmmTFk1\nJS7HqtgdWtXF2vL1VKnidlgqh6WfzFTLsBB/sn2wcHELT7VqR5aWM1P1aqZaH51COtgM6xc8Dxay\nTjMp1TOVwiwdzCrPWKqusVQXrospGhzr7P7BKhPts/HYwsXZHy2UfaZKiKXHzIS1z6SAWeUyG68t\n/cSuazZOzNwDnJqZNBq7p+Wxt2uepYKynR+avmP3cTbm2D2w3ceyvXBstVQvsrTIfZbuYr8H7Lgz\n/YbLYcqNpa5ZX7PKQVZZj+ee59XSwaxv2u8TY8fqFFLAyNrJzDGwNDsb77hMXkd5XeRyWImO6WB2\n38g2whQwS8nl/JyHKfh2X8p9t/HKXt1h9+Rj+Jhl1dQ4bfexNvZZWrRdy2b670xl05lKYWTPHcyh\n1/EZRQIlSZIkSZKsQA+BkiRJkiRJVuCGVQejy1QKm6mqYN81VhXIzKS5zbAQOUvXsGkLU9zftpl0\nMpv/0LeaM9TWwo4PTaGx0L5Dw8hnUs9oprLJTFs8Jjw/MymWlupl1TBmUnyYGmbpRLZ8C0O1bZ5J\n6Zqp0jNTfcnSqi4K159J++R+MlzWUmsOrZZlx47s2m3nj9vGlACy+S0E2b5LM23xWFnIs7HrkJ2T\nmRRFG19svdZ2LE1sJgVhZoyy6pEzqQEM17exZf//Z9LbbD/tWmbneCZ91NLkZtLWuP8zqVu8LnHa\nqsHNOPRe8JhYXzu0mqiNv1w+29dM27lM2uvM9s9sw8yYZtVFZ5Yzht9bkl0fmTZjfefQewLrO5xm\n+g23jSlplg5m6fp2P2e/l2aulTMV5o6J3YPZbxFrw1YRzq6pTGNiihbP58c//vFz5+F6n3nmmXO3\nwap3zYxFVlWSZlKa2F6sf+zfW820vZnqYHZfYtcUbhOPo/VlS7u2yl/czplrjs1j44el5Nm1pXSw\nJEmSJEmS/AI9BEqSJEmSJFmBL2k62ExVr5lUCwv5nAkfteUw1MsqLNhyLFzLtsHSUqyKioWtWfjf\nzL7sb+tMeOdMqsBMatxMNZBDQ98ZkmeVomx/7dxYaP3MPs60m2MyU+HLQkzJ2jZZW+Pam3aTAAAg\nAElEQVR5s3DImeonFl5q4dvWlq2630xbsAopM6mN+9+3Pm+VfWa2aaa638x1zdqNfT6TGkMWdnxo\nn7LvnnIFImPjpl3bZsbfmXF5Jo3UUpGsfdl3Z9qvpR7yc6sOxpTw/fQmW5+lZ1qFERubbNr6zkwK\nDFl6Ho+RnUtLBzo0LXQmZejUqhEZS3eaMXNPNJNyNJPuMGOm2tXMNXXm+m0pvBe9AmLmtQCWemrp\noGTjiFVHmzl/di5538/7Hl6nLCX+0Ipgz5cqmTST9mepWDyHTMuySl7Wr3nemA72yU9+cjfNdmfj\nkW3bTFVVjkVkff/Q33B2HJh6NcZcFU9O23g3U73bftvzWHOafc3G5ZlKaWT32/ab6tBr8XOVLl0k\nUJIkSZIkyQr0EChJkiRJkmQFjqI6GB1aHeLQKlUzy2QYnoV6mZmw+Zn0ppnQaZpJvdnfnkPDrQ8N\nP7P9sVDVQ9MCycLqZqpZmJnzd2hlsWM1U5HEQp4txePQaiBWNcxYe7QKDvYG/UPTYcxMWL4t86LK\nfTMV0ew6ZX3t0Ap9NJP2NZNWRwzHtbB8hlZbxTWaCd3ndr761a++5jJvBKs0ZOyab1XXZq7HM2OW\n9TVL0ZhJb7KUMbv+cJrpTTfddNO5y7d+fVHK4ExaOMPaLR3MQtYt1JzHi+fPxjirHGTbwxQ4a2dW\npcWmZ6qcHprOdkwsvW/m/m3mPsIqxtjxnqnUY/c+1jetfbEdzbyyYOb1DnY8Z6+Bth3ENmz9ztZh\n/dcqTvFzq4Y5U+WWKUwcH2mmgqftr6VhWiWyQ38X3QhMubLxhefHjgfTuJg+xu/yc05zmXaPY/dc\nltrMMZHjDFn1Lbtfsz5ry7EKtDOvEBjDr2UzvxNn0o1nXs1gldXsdRh2v2Lp5bbei14Dcd5yZq5L\nl3FaI26SJEmSJEmuSw+BkiRJkiRJVuD4Y/q2juHt9TOh4+a5CumaSX+bebv7tf52rXUcej5mUnFm\n0kZmwuRsOc9VG3o+VhSaSfuyNLuZyku2nJmKgRYuayHiFgZvKWYzaS9kIagWZj+T7rDf3i0kd6Yq\n0ky6neF6GS5rIfvWDuy42PYwPNrCqa1CiplpQ8/HvnyZlOovSrjxRGU5C323cGkLIbc+wbD5Q/fx\nonSwmUphZJXybD9tnpl0TvYRSxuwVBTbZp6bmfP6fKnwdQxm7l9OLZ1u32XusS/rMq+QmKlCO5N+\nZa81sNSYQyugXcYX4176i4npYDMpk5YyxuXYvTEriDF1j6lbNi5b9S6aqdhrVf9mrtlk92gzaYv8\n/OUvf/lVy7VqXJbCyfVZ2t7M/fRMNepDK+Re5lUqM79zDz1nl7mHO+0RI0mSJEmSJFN6CJQkSZIk\nSbICyylXNEqSJEmSJMmcIoGSJEmSJElWoIdASZIkSZIkK9BDoCRJkiRJkhXoIVCSJEmSJMkK9BAo\nSZIkSZJkBXoIlCRJkiRJsgI9BEqSJEmSJFmBHgIlSZIkSZKsQA+BkiRJkiRJVqCHQEmSJEmSJCvQ\nQ6AkSZIkSZIV6CFQkiRJkiTJCvQQKEmSJEmSZAV6CJQkSZIkSbICPQRKkiRJkiRZgR4CJUmSJEmS\nrEAPgZIkSZIkSVagh0BJkiRJkiQr0EOgJEmSJEmSFeghUJIkSZIkyQr0EChJkiRJkmQFegiUJEmS\nJEmyAj0ESpIkSZIkWYEeAiVJkiRJkqxAD4GSJEmSJElWoIdASZIkSZIkK9BDoCRJkiRJkhXoIVCS\nJEmSJMkK9BAoSZIkSZJkBXoIlCRJkiRJsgI9BEqSJEmSJFmBHgIlSZIkSZKsQA+BkiRJkiRJVqCH\nQEmSJEmSJCvQQ6AkSZIkSZIV6CFQkiRJkiTJCvQQKEmSJEmSZAV6CJQkSZIkSbICPQRKkiRJkiRZ\ngR4CJUmSJEmSrEAPgZIkSZIkSVagh0BJkiRJkiQr0EOgJEmSJEmSFeghUJIkSZIkyQr0EChJkiRJ\nkmQFegiUJEmSJEmyAj0ESpIkSZIkWYEeAiVJkiRJkqxAD4GSJEmSJElWoIdASZIkSZIkK9BDoCRJ\nkiRJkhXoIVCSJEmSJMkK9BAoSZIkSZJkBXoIlCRJkiRJsgI9BEqSJEmSJFmBHgIlSZIkSZKsQA+B\nkiRJkiRJVqCHQEmSJEmSJCvQQ6AkSZIkSZIV6CFQkiRJkiTJCvQQKEmSJEmSZAV6CJQkSZIkSbIC\nPQRKkiRJkiRZgR4CJUmSJEmSrEAPgZIkSZIkSVagh0BJkiRJkiQr0EOgJEmSJEmSFeghUJIkSZIk\nyQr0EChJkiRJkmQFegiUJEmSJEmyAj0ESpIkSZIkWYEeAiVJkiRJkqxAD4GSJEmSJElWoIdASZIk\nSZIkK9BDoCRJkiRJkhXoIVCSJEmSJMkK9BAoSZIkSZJkBXoIlCRJkiRJsgI9BEqSJEmSJFmBHgIl\nSZIkSZKsQA+BkiRJkiRJVqCHQEmSJEmSJCvQQ6AkSZIkSZIV6CFQkiRJkiTJCvQQKEmSJEmSZAV6\nCJQkSZIkSbICPQRKkiRJkiRZgR4CJUmSJEmSrEAPgZIkSZIkSVagh0BJkiRJkiQr0EOgJEmSJEmS\nFeghUJIkSZIkyQr0EChJkiRJkmQFegiUJEmSJEmyAj0ESpIkSZIkWYEeAiVJkiRJkqxAD4GSJEmS\nJElWoIdASZIkSZIkK9BDoCRJkiRJkhXoIVCSJEmSJMkK9BAoSZIkSZJkBXoIlCRJkiRJsgI9BEqS\nJEmSJFmBHgIlSZIkSZKsQA+BkiRJkiRJVqCHQEmSJEmSJCvQQ6AkSZIkSZIV6CFQkiRJkiTJCvQQ\nKEmSJEmSZAV6CJQkSZIkSbICPQRKkiRJkiRZgR4CJUmSJEmSrEAPgZIkSZIkSVagh0BJkiRJkiQr\n0EOgJEmSJEmSFeghUJIkSZIkyQr0EChJkiRJkmQFegiUJEmSJEmyAj0ESpIkSZIkWYEeAiVJkiRJ\nkqxAD4GSJEmSJElWoIdASZIkSZIkK9BDoCRJkiRJkhXoIVCSJEmSJMkK9BAoSZIkSZJkBXoIlCRJ\nkiRJsgI9BEqSJEmSJFmBHgIlSZIkSZKsQA+BkiRJkiRJVqCHQEmSJEmSJCvQQ6AkSZIkSZIV6CFQ\nkiRJkiTJCvQQKEmSJEmSZAV6CJQkSZIkSbICPQRKkiRJkiRZgR4CJUmSJEmSrEAPgZIkSZIkSVag\nh0BJkiRJkiQr0EOgJEmSJEmSFeghUJIkSZIkyQr0EChJkiRJkmQFegiUJEmSJEmyAj0ESpIkSZIk\nWYEeAiVJkiRJkqxAD4GSJEmSJElWoIdASZIkSZIkK9BDoCRJkiRJkhXoIVCSJEmSJMkK9BAoSZIk\nSZJkBXoIlCRJkiRJsgI9BEqSJEmSJFmBHgIlSZIkSZKsQA+BkiRJkiRJVqCHQEmSJEmSJCvQQ6Ak\nSZIkSZIV6CFQkiRJkiTJCvQQKEmSJEmSZAV6CJQkSZIkSbICPQRKkiRJkiRZgR4CJUmSJEmSrEAP\ngZIkSZIkSVagh0BJkiRJkiQr0EOgJEmSJEmSFeghUJIkSZIkyQr0EChJkiRJkmQFegiUJEmSJEmy\nAj0ESpIkSZIkWYEeAiVJkiRJkqxAD4GSJEmSJElWoIdASZIkSZIkK9BDoCRJkiRJkhXoIVCSJEmS\nJMkK9BAoSZIkSZJkBXoIlCRJkiRJsgI9BEqSJEmSJFmBHgIlSZIkSZKsQA+BjsCyLL9vWZa3L8vy\nuWVZ/uLe3/7VZVnesyzLZ5Zl+QfLstyLv/0r288+uSzLg3vfu7Isy/cuy/LI9u//z7IsvwJ//w3L\nsvzjZVmeWpblo8uy/PllWV554Hbft13/Z7bb+I3XdwSS43SJvvmSZVn+52VZHluW5RPLsnz/six3\n4+9vXpblH2375oeXZfmj+Ns3LMvyhWVZnsF/33rgdv/KZVn+ybIsTy/L8pPLsnz9JQ5DcsNZX9yO\nQ5u9/sL+dJlx8g/vLfez27552wHb/R3LsrxzWZafW5bl2/f+dulxOLnRtuPddy3L8qHtmPNPl2X5\n9du/Xdg/sYwXL8vyU8uyfHjv8wvHsmVZftt2vZ9eluVvLcvy6gO3/R8sy/L4siyfWpblHcuy/Fv4\n26XH4uRGusY97L+97XNPL8vy7mVZfhP+pvewy7K8bq9PPLPt439w+/c7l2X5O9txdbMsy33Xsd3a\nL7d///3Lsnxw+/e3d497fXoIdBweGWP8l2OM/40fbm80/8YY44+OMV49xnj7GOOvYZZPb7/zh85Z\n5ivGGD8+xvgXtt/97jHG312W5RXbv9+8XeddY4yvHmPcPcb4kwdu9/eOMX5ijHHrGOOPjDH++rIs\nrzlwGckxu96++R+PMf6lMcY/P8762JNjjD+Dv3/PGONHtt/91WOM37ssy7/J9W42m1fgv++e3eDt\nTfD3j7P+/Koxxp8YY3z/siy3zC4jOULn9kV4FfrLd+Dz6x4nN5vNf8V+OMb4b8YYP7zZbJ44YLvv\nH2P8p2OMv3vO356LcTi50V44xnh4nI1lN48x/rMxxv+x9+PP+uez/tAY43F+cK2xbFmWN40x/pcx\nxu8cY9w+xvjMGOPPHrjt/8kY457NZnPTGON3jzH+yrIsd+Lv1z0WJ0fA7mHvHmP8lTHGHxhj3DTO\n+t/3LMtyZTuL3sNuNpuH9sbFf26M8YUxxvdtv/uFMcbbxhi/+RLbrf1y+w813znG+C3j7HrzXWOM\nv7ksywsusb5V6iHQEdhsNn9js9n8rTHGx/f+9NYxxrs2m83/udlsfnqM8e1jjF+2LMsv3n7vn2w2\nm788xvjAOcv8wGaz+VObzebRzWbz+c1m87+OMV48xviq7d+/Z7PZvG2z2Xxms9k8Ocb4c2OMXzW7\nzcuyfOUY42vGGN+22Ww+u9lsvm+M8ZPjcp0+OSrX2zfHGK8fY/z9zWbz2Pbvf22M8SZ8/74xxl/d\n9s0Hxhj/eO/vl/ErxxiPbbft85vN5q+Ms5vrtz5Hy0++5C7oi9f63nWPk7QsyzLG+JZx9qDokPV/\n92az+cExxtPn/O1S43ByDDabzac3m823bzabBzebzRc2m80PjDE+OM4erl7TsiyvH2P8jjHGf733\np2uNZb99jPH9m83mRzabzTPj7B9l3npINN1ms3nHZrP53LP/O8Z40RjjtbPfT47ZBePmPWOMpzab\nzQ9uzvzdcfYPJm/c/v1a97D0LWOMH9lsNg9u1/nYZrP5s+PsH1iud7sv6pf3jbP77/9vs9lsxhh/\naYxx2xjjyi9YUC7UQ6Dj9qYxxjue/Z/NZvPpcfavigf/WFyW5c3j7Ob2fpnlXx5jvOvAbfvAZrPh\nje07rmfbkhN0rb75XWOMX7Usy13LsrxsnN2s/iC+/z+MMb5lWZYXLcvyVePsX1x+CH+/sg3D/eCy\nLP/9siwvv+T2LmOMX3rJZSTH7EPLWWrlXzgkXYuuMU6+ZZzdZH7fOX97rhw6DidHZ1mW28cYXzmu\nbssX9c8/M8b4w2OMz84sfvz8WLY/Dj8wxvjcdt2HbO8PLMvy02OMHxtj/PA4i+x91nM9FifH4O1j\njJ9aluU3Lsvygm0q2OfG2T/mj3Hte9gxxvX/48iMC/rlD44xXrAsy6/YRv/8rjHGPx1jfPS53obn\nux4CHbdXjDE+uffZp8YYh76756Yxxl8eY/wXm81mf3ljWZZfO8b41jHGf/6l3rbkRF2r/b9/nIXH\nf2T7+VePMf4Y5v2BcRbK+tkxxnvGGN+12Wye/VeT94wx3jzGuHOM8WvG2b+m/qkDtu3/HWPcuSzL\nv7t9yPSt4+xfd152wDKSU/HEGONfHGPcO876yivHGH/10IVca5wcZ2PkX99GHDznrnMcTo7Ksiwv\nGmf977s3m817xjX657Is3zzGeMFms/mb5yzuWmPZc3Ifutls/o3td75pjPF/bTabL2z/dNmxODlK\nm83m8+MsguZ7x9nDn+8ZY/ye7T9ojnHte9hnff04S8X861+EbbR++fQ4+8eYf7zd9m8bY/zubVRQ\nDtBDoOP2zDjL1aSbxzlh5WZZli8fZznVP7rZbPZDbceyLF83zjr/b9lsNu/bfvYWvOzrXdvP3oXP\n3vJcbFtywq7V/v/HMcZLx9n7sl4+zt4f9INj7N5z8LZxNqC+dJyFuP5ry7L83jHG2Gw2H91sNu/e\nhtV/cJy9T+Q3b797zb652Ww+Psb4TWOMPzjGeGyM8a+Psyijq164mTwfbDabZzabzds3m83PbTab\nx8YYv2+M8esOSQmZGCdfNsb4rQP/2jk5Ts6u/xeMw8mpWZbly8bZg9SfGWf98ML+uY2q+RNjjP/o\nvOVNjGU6Dh/aPzebzc9u0zZ/3bPv57toLE5O2XJWyOdPjDG+YZxFv/7qMcaf30bDjnHBPeyebx1j\nfN/sP448F/1yjPEfjLPonzdtt/13jDF+YFmWuyZ3P1svvNEbkAu9a5x1sDHGGNsB841jMlx8WZaX\njDH+1jgbMH/POX//5WOMvzPG+F2bzeb/fvbzzWbzj8bZv7AMfPamve9+5RjjDcuyvBIpYb9sXMe/\nwCYn6Fp9881jjD+y2Ww+sf37nxlj/LFtGPx9Y4zPbzabv7Sd98PLsvzv4+xfO857qeVmbB/Yz/TN\n7Wf/cJz96+tYluWF4+x9KP/d9exocmKe/dfAqX/kutY4ufXNY4xPjLOQ9LOVTPbFifWfOw4np2Sb\nFvJd4ywq4Js2m83Pyqzsn28cZ+PhPzr7+njxGOPmZVk+Osb4uu07hi4ay941zu47n92GN26X8b7t\nfen19M8Xjp9/L8p5294/nuf54M3j7D0+z6ZY/fiyLD82xvjGcZZapfewm21hhO0/nvzWcTY+TrnE\nuMl++eZx9i6wZ//B5G3Lsjw6zt4h9pxHJD2fdTE7AsuyvHBZlpeOMV4wzvIcX7od7P7mGOOXLsvy\nm7d//7Yxxju2IbZjWZYv237+orP/XV66LMuLt3970TjrDJ8dY3wrwuieXecvHWfRCL9/s9l8/6Hb\nvO18/3SM8W3b9b51nL0h/ov5voTkS+p6++Y4eyHetyzLcvO2L/7ecVZl5IkxxvvOFr38tm0fvmOM\n8e+MbS72clbS+t7lzGvHWUWiv33gdv/ybfj8TWOM/3aM8fBms/n7lz0eyY1ifXE5ey/AV2370q1j\njD89zip4fXL7veseJ+Fbxxh/6XrCzbf98KXj7H7rhdv1v2D7t0uNw8kR+Z/GWcrIb9xsNrt3+1yj\nf/6zcRYJ++btf//hOIv4efM4S0W51lj2V8cYv3EbXfDyMcZ3jDH+xt67KtWyLL94WZZfvyzLl2/X\n8TvG2Xu5/uH275cei5Mb6YJ72B8fY3z9s5E/23+MeMv4+XcCXXQP+6xvHmdVw/7BOet96RjjJdv/\nfcn2/2e3+cJ+ud2237Asyxu2ffPXjrP3gP2z2XVka7PZ9N8N/m+cVRba7P337du/feM4y0v+7Dj7\nV8j78L1vOOd7P7z926/e/v9nxlnI7LP/vWX7978wzsr48W/vOnC779tu02fHGO8dY3zjjT6W/dd/\nz+V/l+ibt46zG9SPjTGeGme5y1+Lv/+acTaQfXKcvczuz40xXrb92x8YZ3nYnxlnN8J/eozxygO3\n+3u3y/7kOKvqcOVGH8v+67/L/Gd9cYzx742zSkSfHmM8Os7ec3AHvnfd4+R2nrvHGD83xviK69zu\nv3jO+v/97d8uPQ73X//d6P/G2ft+NmOMn95ry7/9Wv1zbznfMMb48N5nF45lY4zfNsZ4aLv8vz3G\nePUB2/3V4+yls09vx+kfH2N8M/5+6bG4//rvRv5n4+b2b79vnBVBeHqcRdj9QXzvwnvY7Tx/f4zx\nHbLe/XVuDtjma/XLZZy9TuGh7Tw/Ncb4nTf6WJ/if8v2gCZJkiRJkuR5rHSwJEmSJEmSFeghUJIk\nSZIkyQr0EChJkiRJkmQFegiUJEmSJEmyAj0ESpIkSZIkWYEXfilX9mVf9mW7UmSsSnbHHXfspv/4\nH//ju+kXvOAFu+mHHnpoN/1jP/Zju+mPfOQju+mf/umf3k3/3M/93LnL4Xo5/9527qZf+MKfP0S2\nnGVZdtMvfelLd9Of+9zndtOf/exnz52fy3/Zy1527jT35Yknnjh3e7hMbr/ty/4+fP7znz93uZz+\nwhe+cO60rYPzfOYznzl3npe//OW76Ztuummc52d/9md30z/zMz+zm/7Yxz527r5wmuviMeI83H4e\na56/F7/4xbvpF73oRbtpHh/iurj9999//3Le/DfaO9/5znPLBNox47m1dsR5rB3NVCfk+eF3uW2c\n5jaQtRFbjq2L59OWadtMF/VN/s0+n1mHtU+yY7e/Teet19qEzXPocSfbHm4zpw89Vm9+85uPsm8u\ny1IJz6zaZrM5yr75nd/5nbu+adcz3rMQr4W8PyS7F+P9iI0Vdl3nfS+XY/duvCeyayq3jeOj3TPO\nXLNt7OJy9u/h7b7fzo3dN9vxsm3isbN7ad438ty/5CUvOXc5PDfcBrYnO388B5wmfs595zH58i//\n8t30+9///t00f4O9973vPcq++ff+3t/bHeQrV67sPrf71Ycffng3zf3mMf7kJz+5m2Y75G8F6ztc\n1zPPPHPu9lhbmPlNY+33ySefPHf7+ZuMv6XY1jj9qU996tzlcHv4W/7OO+8c9MpXvnI3zePLfeDv\n3ptvvnk3bcd0pr+w39m9H3+H8njRJz7xid30U089de48t912226ax4vfvf/++8/dZk4//fTTu+nb\nb799N/3Wt751N/1N3/RN1/zuPffcc82+WSRQkiRJkiTJCnxJI4Fm2L9qHPpd+xcUPiHlE0KySB2L\nBLKn6DPLtH9ZmdmXmX/psGXus+gJY/tpuK32JPtQ9uR75nzMREvNREfZsb7Mft0IFrVj88xE8JBF\njJAdM5vfIkxsXw7dZtsGLscinA69jl20bRYlMxP9ZuuYuV7Yd+1YW9QYHTqPbYOZifixbT5W/Fce\nsvM544u935fZtjW6zPXUzBz3y/TBmXudQ5d/angPOXP/RhZROjMWc/n2L938nP96znkYeWD3yRYp\nzfk5D/81n/MwgsUiJ+y7/Jd927b9dVgkxUxUEKdf8YpXnLsc7sPMeWKWAM+NRfNwHouGt+9yHk5b\nf7Tjw88ZLWH3GM8Xdi8/c38xc22zSOmZezH7vWL3pfZ7k+uy65hdH+y3MyN89uex64X9nrLv2vz2\nu9Kuj5yH/ciyGSyCzKK9rO9bhBO3gdtm58DGGzs3pkigJEmSJEmSFeghUJIkSZIkyQocXUyfvZzu\n0JeFcn7Ow9AthllZ6N1lUn9sXRa2Zi+J4nIY6jWTVkb7x2omDO/Q1DILcyQLhbXtsTQbfnfmhcF0\n6LpmXhZ+aNjoKTg0TWHmZb+HporMHD8uk/3Fts2+e2i49KF9YuZFmfvrOHS77VjMhBGThf5b6ujM\nS8RtvdbvZlIQ7dzMvMj+0DTYG8FeLJvkxrL0JbLxiPeEfEmrjXd2PbbUD94fzWyn3dPai2vt3mem\ncIZd45k2QVwmr9n7Lz3mtdJexDyTUm37M3MsDn2VA/fZlmPHxX4DWOrdTOEES+Hji30PTTk5BRcV\n6jhvHrKXcs+8+sLOOfF4c9qWyfXyvFm6qKVAcZqpXrfccstu2goacb3732d6p6Wfsa9ZqpSdM+4b\n+45d1ziP/Yad+T1ov+G573b9mnltjbUne44wo0igJEmSJEmSFeghUJIkSZIkyQocRTqYpfjMhDEy\nfIzTFs7J8KuZsNWZFDBOc/n2+cyb1fkmfn6XYXQ8VhaqZuF/Y1wdWmZVsWyfrSqShbRx3yx8zsIc\nLeSRVRtm0sHs7fkz6TYzVdxOuWLCTEUXmknBseN3aFqPbZv1TYbNWzoUv3tRtZHzzFQdMbNpgpZC\nYCmQXC6vF1Z9gGbS4Wx+uw7MpGVZOpylBM+k/l6mAtyxetOb3nTQ/DP95djcqO08xuNzzG34i3G8\nLlMN8Eaze7mZNHFe82bSmS0Nfea1AJYqMVPR0u6N7fxwv2wfLQVmZty7KH3GzsdMFctDU8aYQmLn\nwI4106xm0odmKlrOVOI7tAKxpfqccjrYTHVTw/Z8aCWvmb42k95lv9WswpelhvE3GTE19emnnz53\nmeyPd955527aKuDtr8/uy61dWbudSQ3jejnPpz/96XPn5zGyaxl/h3Ob+V1L9eJzCktDe+qpp879\nnGYqLs8oEihJkiRJkmQFegiUJEmSJEmyAkeRv8KQLoZlMR3MwscYomipRZzmciwUayac3kJkuc0M\nQ2P4p4Vh2hvN7bsz6WwWgrv/fR5Hq0ZjIaMWhmqhzxY6a2k2FgbNYzRT5efQqk40kw526DKPyUx6\nl7EQ2Zk0I353pk1Z+hHNVOOy/jKzTFv+TGqUhZPvt1/2QX7f+qZtk4WX8zpo1187dpZKZ+y82vV0\nJg2T22/Xlpn9PQVve9vbdtN2TZ0JTbcUy5nUy0OvZ4emi9q6Dk1Tte08tCLQ9Vy/Z47joSk0ZuZY\nXKby4sxxnAnRv8w5O4V0sEPbkqVUWKqEtQVLebZUYLtv5PK5PUx3mKlOaNclez2AHZ+ZdEO7N9xf\nB7f7otcinLcdM+lO3DceO7u/tVdF2DYQz7H1C/vtxHVZagxZ2ri9xuEUHJoybu3ZUvpm1mv9guy3\nzsxrOWhmzCW2C36X1wGmT3GZzzzzzG76s5/97G56vw9xWdZ3ZipzcX47N5zf9u1Tn/rUuZ9bShen\nrQohl8Njwe/yc3sW8IlPfGI3fdttt507P7dh5vUm5rR+qSZJkiRJkuS69BAoSZIkSZJkBY4iHezQ\nMEyGd1lIraWG2bpmKg1Z6pKF7zIk00LAyCo4MIzupptuOnd+S6e4qPIRjxH3geuzdBer1GPhvFy+\nha7ZPjDszapN2D7PsO2x8EI77jMpFqfA0idnqk/MzGNpjNYGGbbJc8u37Fv1uT1J8F0AACAASURB\nVJtvvvncbXjyySfPnYdtjf3XqnK9+tWv3k1//OMfv+b2cJkMr2Vq2Bhj3HLLLbtpXju4XB4Lhpty\nWfyuhf5biDOPr4Xp8vzxu1a5gNPWtqz6I9vETLqRpYZZuP6xYqqupeJZisRMRUS7Pj1XVcbsmjBT\nmWUm/WhmXTNpOzbWfbGu39ZHZsYvm+fQCoUzqWoz6a/Wng6tamRpD8dqpmKhmUnTsD54aHUm2x5r\ndzPLn9lme4WApcbMVOCdxXNjqViWTjNzr2tjiqWrzLzuYCZVaWY8tfv+mWqbNFP16ljNVAyeqVZq\nZo6xTdOhr5SYuU7zXoz3gPY7ye4NeS9p93oPPfTQuctnhesxrr7Pvv3228/d7pl0MPt9bvPznpn3\n37wm2O9T6788Rlyvpc+x4hrZ62Bm0ktN6WBJkiRJkiT5BXoIlCRJkiRJsgJHkQ5GFsLPUKlXvOIV\n587PsC+GsVlag4Vz8nOGjFlKkIX8WUgeWXgmw8QYSsb9svkZwmZhbvvft7ea8/s8jpa6ZSF5Fl45\nE97GbSB7MzznnwnBtPA5LsdS9U4tfN3MhEVb+KuFmFooO5dpfZNtnu3x6aef3k0zZPJVr3rVbpph\npzxv1t4tfNvarFXVe+KJJ87dFwstn6kCM8bVx47Hi6GtFs5rfZnXEX7OZXJbuV6rSnBoCDWvrdY+\neKxt2w4NIbb+e6w++tGP7qZ5PWMVi5kUYEvPtfSomaoddn21ZR5alWwmnYhsXD40Tcz296Ltmwnl\ntzHLUqusj7M/zpwPux7NpFHbcbR9tPSemTZk2880+GNi7XYm9cfMpLHMpFPZWHZoFVO2NRvfybbH\nxhmr5Dtjv73MtCXbVttujpXWF+ze2NKEZq59M/ciVvXSUoVtOXaPYr+RZtrxMTk0nXmmOqv9Pjg0\nzXdme2ycmqlISzbuc9v4O/rWW289d/mPPvrobvqxxx7bTV/URmZST+01KIdWquW6OXZ88pOf3E3z\n/pPXI/staale9pzCqjbyNwOvLfzua17zmt20/UayYz1z3aPjvwNOkiRJkiTJpfUQKEmSJEmSZAWO\nIqbPQg4Z6sY3jbMiD0OlmCZm1XksDNPmYZgYt4eh8pYyxs+ZljGTbsbQMH6Xx8FCOC2EfD/018LY\n+H1O85hyfgvzs2M6k0IwU5WA4XkWemfh9xYeb+l8PN+WJmH7OFPx4ZhYRbzLVLPhd+1c2XkgnvNn\nnnnm3M9nqroxnNPalC2ffZAVwZiqZttAPA6sgHbRsuz7dj7suPBzC3HmueH8vB7x+sB5iNcNS0Ww\n9IaZqjZk6QeWAnNo6OyNwP2wfbLKHTbeWSqipcrNpIVaCLq1/5lts3HGwvtn5qeZNIH9dGRLtZip\nZmRtb6YCCJfJPmXtw9LNZs7ZTGrBc1XtayY15ljNnKuZynS2zJnlz6QcWRuxdmf34Zaab/fSxiqd\nWcVeS0Pbv/ewsYP3bHbdtO2wFLWZyos27syk4dlxt+/afTu339JIZ8693QMfK+t3M9dsq55qY4q9\ngmPm/uLQ66tdW2zc5P0qX7Mw85oN+33NaaY0cV18RcMYV6eWWZ+y3wM2jlhf4PKt0jRxn3nvbSms\nPK88RvZ6GusvVgmXr7SYeZXBzD2zKRIoSZIkSZJkBXoIlCRJkiRJsgJHlw5mIYd8ozZDtPg5Q9Ts\nbf0Wgm1h5BZKyW2bqZbDFAqGcfG7FobJNAvuL+efqQi0H15oobBcH0PgGOrHdVvKlaWDWWjmTLUU\nLpPpOpbSMlMh4zLpYFZVy47JsbL0JQtzPXQ5dlztnHB+tsEnn3xyN82wTdtmth2Grc5UCXjqqad2\n0zzP7IP0sY99bDdtqalkx2EMT8ObOXYz1UwslNmuZfyc/c72h9tgYfP2uaXI0kzK4kxlnVNgaTGH\npmEeepxmKnJY6sNMmpGlANr8ZG2ZbWem4piNuRdds20/Z/bZjrvNY2Poc2WmyqeF389UBrQxw9rW\noZWrbrRD03oOrVhFh1b4mknBtjQLS8U4NA3IrvFWTcnMVmHlPbqlw9m+2b0+t3Xm2Nn1yK5Nlj7H\ntsLxlPf3TCO3Y23pq2T3D2RpdMeKv2PsfsTu8a9cubKb5nnjPaelyFtaPH8/Edum/cayVxbYueXr\nDuzelW3HXhXAez3Ow3SlN7zhDbtpq+K9//+cj9tKd95557nf5X7yHt368oMPPribZqVV+/38yCOP\nnLuu2267bTfNV9JY+h+rknE5/A1jbYXLsd8S3MeZSoLm+EfZJEmSJEmSXFoPgZIkSZIkSVbgJNPB\nGO70yle+cjfNsKmZN4hz+Qz/tOoJDCnk5zZty7HwYHtjuqVZcH7uC0PGrFLMRdtkqWUMVbR0OKvu\nYGlpFsZmoaf21n6ul+GbM1WsZirDcb08BxZOf1GqzzGaqcQxk1oyUz2B01YNgHgsWY3r0UcfPXdd\n1q/ZLxgKy4oJ3H6mjFkILqe5TKuSwG2z4zzGXHUWq1ZglRFnKvpZWDA/ZzirVeLj/vPccNtm0kKt\nnVn6H1nKmKUDHStLnSE7fnYNtrQAu07PnIeZtA47VzPj9UzFPTs+M9dgS1G5KOXH2pUd60PTuyy9\nZWZ8sfsDa/Mz6XyWhsTtsbZoY4Od1y9G+ttzza6p1u8uw8ZHbsOhlYloJn2fZs7PTLqkret6qsZZ\nG7ZXDVjKzUx1WkvvsrHpMu2c5/hDH/rQbvq9733vbpr3Ga997WvPXY5dl2aq0NEppIPx/sjOiV2/\nrZqznTceV37XXlNhlaNsXVzmJz7xid0079F4Tuw1Hkzjsntj3vfa9vNze6XJ/rhkFXnZDu03Nr/L\nvsB95n2pvcaC6WM8Fly+pRHyGcR+5bNnMV2QqXRcPufheZ15dckXQ5FASZIkSZIkK9BDoCRJkiRJ\nkhU4ilh4hjvNpCgxdI0hWvzcqgFY2gTDvuy7Fl5tKUEWdm0pU1bRzNKSuBw7brau/flmqnfxGHFb\n7a3sFrZry7dts/QeC6O01CYLq5tJCbHQSQsHn6mccqxmqpDYObyoGt2zLMTUwtE5D0M++RZ/uuOO\nO3bTDMPk+WFIpvUXhnszDc3CTjnNcFHuF9NXZ/H4MnWN6We8RrCt8vrI6gbsO5ZWZqHSPHY8FpyH\n+2nh98RjbakiPDe2bcba8alV7rP2Zte8Q1N8ZtIpZhya1mFpCjOpprb9NJPuOlNFZ5+1JTteVmnJ\nlmNpXFY99NB0QUuBs3H8Muk6dp9kKRanUB3M0tYPHfNn0mtmUoisYoyN3XZ/eOiYfihbzmy/M9ZH\n7HNuB4+FvZqBbMyyVE0bW7kcuybQhz/84d30j/zIj+ym77rrrt009/fuu+8+d9tmrqdWMW7m+Bwr\na2O8jrLSK+/l7L6U90Sc5vHmfZadK2K7YGoR08G4TKvwzG2we0PuC+8x+V3eS/Pek23cKkuP4df2\nW2+9dTfNV7pwHqZxMb2NbZLbZxXHuD/cB6vSxXPP9DH+BrTqulaVzNKoWXHMKgrbqzR4Xg91/KNs\nkiRJkiRJLq2HQEmSJEmSJCtwFOlgZGHLDJMkq5ZlFROsEpmFXzFUj9tj4VcWRsvtse9aqhO3zcIz\naTb1wcJiGcbH48swvJkqYBZiahUsLLTaUrHsDfVWRYUsPNrSkzhtFews/Hg/LPLYzVQamqm6Zstk\nuClZdT8un6G2DBG1SgqWHmFVthjyaaGmDO1k+2L/YL+xfmDXlv35iCGsjz322LnbxzbJcNZbbrnl\n3G1iWKldsxguyxQwVmjjeWLqGY8pj5Ht/0zFQG4zWUqPHfdTqEB0aIUlC3e3Y2DzzFRgs2uCpUXb\nuGH9new4HFrZhm18Jr3uonHTjqlVzaNDUypsTOSx5rVspo9bCtOh13dLSbMx1FJOLN3s1Nh9mh1L\nfm5908Yya48zaf22DdeTGvmsQ9NOZ/vdecvZ3zYbF+xed+YemtvBcdbuM2bS9riddq/AZbIi2A/9\n0A/tpj/ykY/spjkus49/3dd93W769a9//bnbY/fedGi63LGaqd5m9+w8b0888cRu+qGHHjr3u+zL\nN9100zW3hyyt0H73WEoi98v6B9spf2OxvXOZlvZl1bHG8NeJMB2MrxHgseP8lvLKebhu/t7gMbJX\nrhDn4fZbFWH2wccff3w3/fDDD++meR/O88F7YzsOM69SOTR1v0igJEmSJEmSFeghUJIkSZIkyQoc\nXToYQ5ksbJlpIxbaz9AthqgRw/D4pnSryGEVhSwM3MLjZ9IarAqYhQ5ahQFLddr/G1lFHh4jC6lk\niKylY1hVBUtF4P5wG7idXK+FSNo5YGgjQwe5LoYaWkqhpSbOVP640Sw1w1IwZtLEiMee54rHzPoU\nl2lVDyykeialwCosMJWK62JoJ9v+7bffvptmOCfbu6Uq7qeIMiSX28cwV1YsY2ocv8vlWqqmhQtz\nmsed1SlYRYN4Pmz/Z9qcVXYk2y+rjjOTSnRMZkKAydo8P7f+NZMOZuknM6m9lhI0s/0z1xY755ay\nbNe0iypGzlRfs3Zr83Ob2Nc4//74fa31Wvi+XU8tHWgmDYmpEdwepkDwujRTBekUzKR62fmZGZus\nf81Uc7VxcGaZxL5wmUqndm0ha3cz17T979i6Z5ZlfYFjMe9j7Lpj94FWrcxeXfFTP/VTu2mmozO9\ni9vD9CRWB7vnnnt204eOJadW8dZS1mbaiI19TI9ilbb7779/N81zy4pPr3nNa665Lm4bf4twvZbq\nxN/FNlbwem+/ne06bdcWbhtTo/bvaVkRzX5b2bjLe3GrGsbzzdQqq5zL1xRw/OIx5W9+7hu3gSlg\n7IM8dnw9grVLbhvHzZlXz8yk+pvTHX2TJEmSJEkyrYdASZIkSZIkK3B06WCWpkAzIeVkb0onhm4x\n/IqpDAwNszByhp4xTM5CzbltFqJvqWEzlXAsnG+Mq0MGLXTcKklYeDH33/aZIXN2/mbSDLhv3Gar\n/GSpevwuz5mF7zJc0rbH0t+O1UzKifVNS3tkG7E0I6u6Zul3dg4tbZHTM5WJOL+1faZAcX8tBYz9\n1Nrm/rq4LM7HUFWmZbFqmKW7zITdWuUVqwDBY8F+YSlgPAfcBkv9tfQJSxu2NjGTCnesrD9aGz70\n+NlybBtm0sosXYXtyNKhyM7PTPrgzLSNm7b9+/9vKcZclvU7jrkM8ec0l8/lWMVMu1ayUglD6Mn6\n1ExaEZfJaxlTICwdgmbSdo7VTJrkTIrTzPJpJk3Spg9NxTs0Jcjmn6k8aCmVlo550fetn9q11VKA\neO9qY7lV8yGrBGyprQ888MBumqkuX/M1X7ObfuSRR3bT73jHO3bTVkWU9xLss5YORFZ195gcOs7P\npNTw/POayiptvIdkWg+PsaWDsU1xHOC6OM12Z22Q95I85zZGzby+wraT4wDT0/a/wzbG7eBvMZ6/\nO+64Yzdt99NWfYzLZFoZp5kOxv7C5bMv8Lxy+1npjPPY6yR4387zxLHy0CqwhyoSKEmSJEmSZAV6\nCJQkSZIkSbICR5EOZuHlFpJt4WpWpYpv9eY8DDHjei0U0FKIGDLGcDiu15bJ71qVMWJIGvfFQvov\nCtm16lrcB4aoWZqYhTtbpTCGLVoopFV+sko2Fp7IcDtbJr/LcFni9jMcd6Z6wqFva7/ReJxmQkOt\nHVoIOpfPsFBLDbP2bOl6lrpnVTu4Ls7PcE7Ow5BaS28ys1VOLGSd67ZrEI+RVa/jtnIfeN3hebJ0\nUaaGHRribxXEaCak29Zr6bJWgeVY2bV9JpXJ0vts+Wamao+dB6uoQwwdPzTVlJ+zHVlVTeunnN/S\nT/b/3yqJWuopl8t0Toags9oI2yqPESv+cN/Yp3isH3zwwXOnuXy7hsykBxND4rltvJfgds5UDDxW\nM/eoVg3W9s9SIw9N3ZoZi63yl40JM2k1M9co20fieu36tt837R7M0mIt/YrTM+Mg2dg3UxHN7pNZ\nCZR9h79hrAoy7+etuqrtu23zzPm70Z6rbbTzY1VVeVwtVd1SR/ld/n5kJTKm/RGv90yH4vL524Vp\na9wvq5ZsrzqxVxHs477Z97l9HO+4D/zuzOs+uF7ObxU5OS7TnXfeuZtmupbdu1jaGiv3kd1LHZoq\nf6gigZIkSZIkSVagh0BJkiRJkiQrcHR5Klb5y0LBLdzO0psYosXwMQspZ5gYpy1clG9uZ1g300zs\nzfpWRYdhe1YJxELfL0p94N8YSsrQUwtHt/QK7pulrlhItO0PpxlqTlwmwwW5X2wTVpGBqWTWPpgm\nNFN149Aw7hvNttfC1y11yY6HVY2zVNCZCkHsI2ynDIu1UF5rd+xrDFO1dEB+ztQHthcuczack9tE\nVpWQVQ8sBcza58w5mAl3ttQ7q5BhlcWsyp6F9c5UHZpJLT5Wln5q58qOn12D7XjY8nkeLJ2K8zD0\nm2MCr82WDkbWl2+77bZzP7c2bumJF1WwtDBvssp3HJtYWe+DH/zgbvqjH/3oueviNYWf33vvvefu\nA+exSjM8H5beYNdr4njK+a9cuXLu9phTSAEju3+xtH2ytm19cOa6ZVXdOH1RxdjzzLzKYIb160Pv\njzj//nGw9m/VJ+36aPerlj7HdE4e31e/+tW7ad4fzKQJsc/OXGe577wH4L4wxYipZHY+LB33uapM\n9KUyc12x6x/PM+9T7J7Tlmm/++x4My2JYwLPod1P8ZyzTbEN8vxzvyx9kOvl7yq2U3uNwxhXj31M\nbyOO3xzvOB7zuHBbuR1cF3+Hc5ncH46JPE/cB373lltu2U3zGHF7OL7zc6bzsW3xWsFx2Sol2/X3\n0DH0tH6dJkmSJEmS5Lr0EChJkiRJkmQFjjodzMLUOW2VRxgOxrAv4jwMAWNYHUPMZt6yz7BQzs/Q\nQS7Tqhlw3xlqyOXwu5Y+ZW+J399uC2NjyBlDTBn2x3mYAsbjy33mei1FgdvNkEeeb4Y2WvUpbg+3\nwdIb2IZ4rDn/zBvprXLVKbj55pt302zn3G9re3Ye+F2mRxFDUq2/E4/rXXfdtZtm5Ryui+2R7cL6\nPveL1QC4Loaasj3ec889u+nXve51u2keT7YX9qcxrj4WDEPltcBSS5gewn5k6Zw8N1wXv8vrg6X6\nWOg4jx3bllWFsGpHnJ/pM7xuMkzXUojtGnqsrJrNTKU5Szlh27b0CLZVtjVe7y3dkufkiSee2E0z\nDNyqR1qql6VwWgVAth1up6Vq2XVgv2Ik943LJR5TXmsYds4qXQ888MBumseL28R12VjJfsT95OfE\nsH5LJeK+8JxxG3isGdZuFce4zP1r36myNCVLB7NqMLPVXc+b3yrM2n21paJYX5vZHkvvsop5llo0\nk4a4nyrN+awN27G2VxzY/eFP/MRPnDvN8fdrv/Zrz91u9hf2Tesjdm4snZXXBN73sr/PVN6kmXN2\nrA7dXqucxvbF+yneN1l6+szvA6tqzftM9muroGXVrngvxvl5DbZXi1hVWBuj9vE73De2fx4jS93i\n60q4b1w+x1nOz+PFZfL+lueD94rcHh4vLp9pe+9+97t306wIxuPIZdrrDnhMrUqrXa9nFAmUJEmS\nJEmyAj0ESpIkSZIkWYGjyFOxCgsW5mpv6Ld0ME4z1I3zM6TN3tBvbyK3t4MzfYHhiJx/Jv2N4dUM\nH7N0pZljtY/hhkxp43J5HBlWx2Nnoac81gx1s/PE0Fa+MZ7HztKKGFbH0EyulzgPt5/nzNqNhdFa\nBZpjNbO9FhZuYcIWgmsh25bGYmlGDFVlW2AfsVQ1S+mzUHmGf1qK5Ux1v9lqGzxG7AtcloVzc908\nLjyv1u+4n1bxh9PsO9bvrIqVXXN5fLkvlhJgbcjO5alV67P0Z2s/M/2UKVRWyYjXQktRuv32289d\nDtvCo48+upt+//vff+6+cNy0VBpOs11wGzguMTXQ+pClrBJDvMe4un1atUpi2DkrgzClkZ+z71iV\nUx4jhvgzZZvXQc7DawLPjVUa4nll+D3vGXisrXqipYBxmudjtnrijWRj3EzaFNm+8lplqQCWWmJp\nAVYp0cZWS6uy67FVmrLx1MYWS42aNVNd2FLAiG2SKR4/+qM/eu787FM/+ZM/uZv+Jb/kl+ymmVLO\ndFGrtGqVC616L69RTI2x+1s77mTpvsfK+uZMlVhLa+LnVrWZ58d+lxmOm/bbk8vktdwqVrOvWZ9g\n++KY9pGPfOTc+a3SGfd9v89alVt79YOlZPNY2G9+Hjv7rU7sR/b7nPPwtz1/53LbOFYyZYzYZ9mG\nOI5/sStmntbdcJIkSZIkSa5LD4GSJEmSJElW4CjSwchSmaz6FcPnGMbFMDELUbNKPVYNwFKdrNIQ\nw/AY0sVtZggfl8mwQ26nfddCxi4K4eS+MbTbqvZY+hzD6Hks7LhwOywMj5/bsbbKLrYuC6023K+Z\n5ZNVCjlWdpxoJr2Gy7GwW543hqHyu2znPOecx1IKrLrBzDm042Dn0NLBLPSd+2vh9/vz8VhwvpmU\nMaZp8LtW/WQmLcFSrth/rXqihe/yc4bIch8tXcXC+K0dnDIL554xUw2G54rn0Ko7MmyZabtkKWZM\nk2J1O/YXS+Pg+Mj5ea1gvyEu0+43WLmL6dFjXJ1aZdd/tkmGhTO8nvtvlZCY0sXzwf3ksWOaCcdu\nXgfuu+++3bRdE63qG7eT47JVJrLpmTHxFPrsofsxky5NM/PMjEHW3y0Vw+5XL3PeZsZBtuuZ9N/9\neWZeLcF9YJufqYj3jne8YzfNSpd33nnnbprpYKz6x7555cqV3bTd0xNfCcFlsg8yNcheA8Frjl0f\nra18sdNSnmuWWkQzVZXslSOWgs/jaunsVsXPXlnA8YTXdY4PZJWv7XUlHMetQq6llc1e17kPTH3i\nseByraq3vZqD+2ZpkpYua+fGqqxx+Vwv+6OlnvF4sV/zHHAetjOrRk12P2yOf5RNkiRJkiTJpfUQ\nKEmSJEmSZAWOIh3M0i6sYpGln1goO0NMGYZm4VRWMYChWJYqxFA1hu0xlI6hepaKYmlPFu5rVZC4\nbVzmGFeHtzFMn9vH/bQ0LobkWUoIt5uhwDwHlkJi2zbzBni2G54PC7Pm9sykDFn6kFXvOAWW6mUh\ntVbNx9L1OI9VCuM5ZNgql2l9jSGyXD7bslXJsNQrSz0jtnGGoHJ7ZtrOGFeHdrMtWYopP+d3mQbC\nNDGG2lrVHs7P/WH/tbQsC1vlcWfVEh4LhtlbxTULTebnNmZYdZhjZWkNMxVvZvbVxlC2EVa34Lll\nGoSlb3OZPOdcPkOq2e7s3Fqap6U4kKVI8/MPf/jDu+mHH374qu+zL9gx5XZwn5kOxmsc943L53XN\nqpAwHYypa6zywlS9r/zKr9xNs6/xOFo6qlUY5Dbz/PHax2VaFaSZFKZjMnO9ses8r5fWjji/pU7b\ncg5NSWeftXtO61+Wmmz385bOyW3m/Sn3l21qf9y0yll272LHyNKcWSnw3nvv3U2/9rWv3U3zPptV\n//hdpoZZijfPB/svU9J4HbD7Hv7m4bax/3IfZ+7zTuGedqb9WyoS2xHPg6X7cPmcf6by1aGvJuA9\n3R133LGbZt+015hwfGD75T0275OtXbCP83OOV/u4HZZyxWkea87PsYbtmfco3CaOcZZqzmlem6y/\nWEVLS+Gz5TMdjJ9b+7PnDpdRJFCSJEmSJMkK9BAoSZIkSZJkBY4iHYysooulQZG9RXsmXNyq0BgL\n82NIJkPluf0Mc2V418zb+m293H6GoXH5DGfb3w6Gn1mIvKUWcH0WzshQN6arMHSc83PbGCJo6WD8\n3FKGrIKUhaazYtplqvKcgpkKX5aWcmi/m6k+YCGiXJelKzH8k/3CqlRZVQh+19qOpaxaWhXXy765\nf81hCDfnYz8iro/XIIaRWyqsVUix42tVXrhvVrWRfZbh8QxHZogs+zJDn+24WxqLpTqcQgUiY+32\n0M8tJZfh1UxpIh5jG4OsOgnbgqV9WZqJVQtlm2V7n0mX5r5/6EMf2k0zpWOMMd7whjeMa+F4xLGM\nIe7cJlYc4zRTuricd7/73ecuk6lhTFFh+sk999yzm2ZqAc8ljzVD4pkmx2uRpYBZSryNH6eQAkY2\nVlragaWoWFUozjNznGbuY2fSt2cqRM2kB9k9o+2L3efbfeV+BR6rMGvj8UzavlXu47XD5rFUL7sW\nWzqk3a9yHkvLsZR+Sz2bqbBmlduOlbU3S+Oyyss8foemHnN+S5efSannMm3s49jNcZbsNw3HLqZs\n23jNceCidsHxmOvjOvibi+MX2W+MmXtabp+9coHHziqL8XMeC97HchzkPTzb00yVaqv2dehYYk73\nDjhJkiRJkiTTegiUJEmSJEmyAkcR02chimTheRZaZSFjDG22tBSGbjGUzNI9uA1Mv7j77rvPXY6F\njzHczsJ0Z8LwOL+lYoxxdegdj5eFP86Ey1pYLEMVGYLO/WQ6CdMGLB2M01YVgmF7DP9juKyFgbId\nWNqIpYY9X1LGZqqcWPihtQvObxU52B7ZbnlOmDZk1WnY923aqgRYFSz2O6teZWlu3F8LDx7D02Nm\n5mHYrYW88jpolQjtumzhy4b7xpSWhx56aDf9xBNP7KZ5Xrlt7LOWqmcpYJbyeQrpJ4emd1nftLGV\n55CpRTwnnGb7munLln7F8/YVX/EVu2kLtWYftPSOK1eu7KbZ9q2N8HrCfWQKGMeiMbxyEvE4Mtyd\n83McZKUWpmsxBZKVXTh2s+IYP+d62c4Zps5zxv3iOba0AY7LVg2R52am/Z0aS8eZGf9nKizZdXcm\ntcq+O5OKN3Ntmbk3tPummfQ3Wyb71oMPPnjV33h9YQokWYoirwU2Xlj75/7YbwOrhGr32zOVEe33\nyUVVms6bf6Yqs23DsZpJh7T9sHs2Y8fS+pdV3bLzaeeH9zi8BvMeldfXmWplVrmMy+H0TJrjPv52\n43e4Po7fvA+033qWnjeTtkeWPsY2YdWILQ2cOEbbddzuqw5N051x/D05nSFkLwAAIABJREFUSZIk\nSZIkl9ZDoCRJkiRJkhU4inQwOjTliOFUDKFiWBnDPGeqAs2kOzDcjmHqTGVglQ/bNqt0cGhFM7J1\n7aeDMazWws8sDJEs9NnexM6KJzzW3B5Lm+ExmqmsxnPDqlEMxee55PIZtjfzxvWZcOpTY6mXM2lw\nltJoobAMbeWx5Hm2dCWr3mUVuKxamYU/c5lk1SUsFYlse8bw644ddzsWbNu89vEcMNSW22Hhwhae\nam2F54DrZSUjprQwRZbbxqpM7MuW6sNUFAu/P2WWRmDjo6WBMISZKVGs3saUKGsXdv6t7XCsfMtb\n3rKbZqg1t5Ph7sRxhstkShPXaynSTAGzCp77LG2Ey2V75rFg6jjTwVgVhfcQbOes0mXVx5gOxnPM\n42gh6FbhyNLyGRLPvmnnwFJsni99k2zssNB+61Nkrxew5RjrszbPzPXe0qFs7JqpfsPvsp/+8A//\n8FXfYfv8+q//+t30fffdd+48xG21FBpLWSeOuTZWWhqmVZXjWGaV+2Yq1VlbnKkCNtNWjpVVT5rp\na1Y5jcfGfhvZ9dXGFLu/tfNv12mya62lMHIMfdWrXnXuemdSePf7O/sL70XtVQ78zWhV/6xqILeV\nY5NVVuN3uc/cHrvvsWcB3C+eb94PkJ2bg9O7DkzVLBIoSZIkSZJkBXoIlCRJkiRJsgJHlw42E2Zo\nVcCs6odVBSKGmFkqGddrIWAMYbOwa4Zsc5osBcpCPi2FjdvPkPAxrg4lnalmYWFmFiLMY8T0K6sO\nxtBxhurZ+ZsJp7a35zPkj+tl2gPPK8/BjFOrQGRVToihpxZSbfvN82YpmTxXM8fPwqstNcZCR7ne\nmfBaqxpn02THbT88mO1tZv/JQpwtrJTnhvOwXzAs1j63qkk837zmMF2F/c7C7JmqxJSTmWviKTs0\nDN/av50TphM9+uiju2lWo2IIM6/f1mfJUiYZps0qWPyc55ZpYhaOzu/aceA4yDbINBNL4drfH+K2\n2hjPZfE43n777btppkuzr3H8YsoYUym5D9w3nm9LM7BUL7ue8HxwPOU9EMdQSwc7ZXbfNZMObGOW\npatYSoul79u1384zU0K4LzMpyJY2ZFVn7d7AXoPA+Zmm+r73ve+qbWKbZB+23wyWOku8j2V/tNQd\ntnniPNzOmeo/rHrIvsbx1I6vXX+t3cxc306h/x5a0ZfHj/dfPFd2H2TpYLZeS2mycY3rnblPntlH\nq4LFeyumI7OaK1nVsIvm4/o4rvFY22sEyK5xvA/g2Mpjbb8NmA5nVfasuq5VtbbXT8z8prZrgqX5\nVR0sSZIkSZIkv0APgZIkSZIkSVbgqNPBZlIqLB3M0oZmqokxvItVPhieZtUwLGyPGF7KEFzivnAb\nbL8snY0haftvJbfKSRZmNjOPvTWdIXYMd+f2zVQSsdA4q8zE48uwQwtBPHQbTiEs9jJ4Pi382UIX\nZ9qkhaPPhEhbqoGFmrO9W/gk2w7bwsxxsCoSdm2xY7I/n4UaWxiurduqhFjKgVVtsOpjhtvGtBSm\nyfBzq+TEUFtLybNzb2H8do0+ZZYCxjbPlChWaWPVKaaG2fFm27RrpPUvuzYz3YFjNMdcGwfZHrk9\nHE9Z9ZGpJUylYv/j9ozhKXmWwm0V63jfYOnJ/C6P9V133bWbZpUx7gP7FMd+6xfWd3gsuEyrisrz\nxHnsfu7UKg2RXTtn9tXSaw6tzjpTSWYmpdhSwi0d1a4zM9U8rTIP2ymXyXZ00f0g18drHO+h7RUB\nds7YR9jOea20ir+2D7wOWHoIv8vt5HXT1mXVdfk573vZtiyl59Tuey3lysYjq9RsqX6cPjQlndtm\n87CNWEUptjvbF3sFwcx1xirdWXu5KC2O3+dyLf2b/YvHyLbPxh2rcMZ0d96Lsl/w3Ntvcp4bptbz\nc6t8zX2ZeaUFHTr2mCKBkiRJkiRJVqCHQEmSJEmSJCtwdOlgxLAme7O6pYBZhQULbbXUDKsOxhBR\nCzcjSzNhOBvnYQjYTDUDe8M892W/Oth+RaLz1mFvUycLr7SQPE4zrI7hjzwuPDcWAsewOks5sEor\nloZkLLzy+YLH0sJibX4ePzuuVo2Ky7FqI1ZFxVI/yFLGLBTfUojYj6zSDtuvXce4nUy5GOPq484Q\ndEu/IfvcrpUMVeV6LZTfQs3JKhRy2q7FnOb+8rhz+3mdsbQ42+ZDQ2dvNJ7bmaphlr7B48RrMCuw\nffzjH99NW7ixpdNZirB91yrwWCoVv8t5rMIR52E7Yvg2q2yxPzJ9eQxPhZ5JCeD2sYqW3UPwHHCZ\nTAHj9cGuj0yl5P5bdTC2CR4Lq1rIc8ZwersOEo+JtdHnC+sXNgbNhPzbMmeur3aML3NdnOkTlmbB\nafYPq8bLlMr95bItsQ1bOrPd+7E9c5s4ln3oQx86dxv277nP2zZOW0Vhfm7p0jy+M5WJZtLdT60i\nmDm0iqulP9urI+w3gY2/ZFW6eM45j73Gw9ZLlgLG5bCqJKd5z8V+YylZ++z1BWyfTKHicjk+2utU\nOE7bb2l73Ye9NsFSvLl8Xlvsftgqus1cZ2fa0EybNqd1B5wkSZIkSZLr0kOgJEmSJEmSFTi6dLCZ\nUKaZFARieBenZ6rKWCoWwwIZmm2pMQxhYwg2w9P4Odn+zoT6c1/2Q1PteFnqnYUsWyi/hdFayKBV\nILIQRksb4TYwdJjbY8faKs/Z2/ZPubLJDEvfYAoR2xjTKx5++OHdNNMLWGHG0iYY8sr5+flMG2R4\nKcM2uV9sm2x3lgrHMF2mWXDfH3/88d30LbfcsptmH+Q2s23ubyu/w3QdC+vnuWEbtipd7Avsmzwf\nXCb3k9vGfeA0+yC/y/ZkKYictmocVmnD2spM5YVjYumQMxUarYIPzyerZDD82449WRiyXae5nbxu\ncHomlcwqNxoeB6ZYsQIa26aN+2P4uGkpo2QVgnjdseNl1yyGyrNPWfqkje92P8G2YlULuT3cr5l2\neWopmWQpNdY+LU2BeFyteqyl8Vk1OTvPls45U1GI28+Ke5byyzHBrmlsR9x+XpesguUYV+8P12f3\n9/zcqntyO37RL/pFu+kPfOADu2mmg+1v07NYiZD3Qxxzbb083/fcc89u+r3vfe9u+u1vf/tumhUf\n2Wf5W8X6u40rM2ngp2zmOmTjkY0X1tfsdQc8xmwXljJr22a/S6xfc5lsm7z22+tQyFLYxrj6npP3\nsbx2cH1Mc+Zxsd90TA2daZ/2agb7rlVF5XniPROPl90D2/OCQ6s80qG/SU939E2SJEmSJMm0HgIl\nSZIkSZKswNGlgxl7s/hMmg5DsezN4hYOaevichiGZttgVbNmKoXRTEUY4vbsVwPj3yzs2MIHOT9D\n8hjyZ+khnLZKUbZvPC5WBcxSg+yN9jwuVkGJIbUWTkynlnJCVo2NoYuWAvbII4/spt/5znfupnk+\n2V+smg3bkYWaW4ohl8/t4XLYdm6//fbdNPeRqV5sX695zWvOXT7Dwxl2zXQNhrsyxHu/AhHD3++/\n//7d9IMPPribZruyUFuG9lollLvvvns3fdddd+2meR3kMllFifvD88H9577xHHD/LcWO/ZHLYVux\nyg6WFsftPIVUFEsLYF/gubLULR6zBx54YDf9jne8YzfN9mLV+qwKJ4895+F3uc08z0xfsGojNhZb\ntRyr3sL+xHQKS33gMsfwlBumX7Gd87hYVUoLI7eqIjxGbM/cZ/ZZG6+t77Cd8frOc8brIK8bXD7T\ncWeqnJyaQ6u7kIX8W8XMmW3guqxCrt2jzqQH8XO2WY4/bFPss2xfXCY/57XL7suseu3+962Spu2P\nHUdu33333beb/uqv/urdNK+n7OOc5jHi9c6u19Y+mJLGa9l73vOe3TT7HfsyrxV2bz9TdfTU+q+d\nZ/vNMdMH2SZnUpLtVSR2fbC0J7L0zJn0Pru3t6qdbEf71WzPW87+MbFqXFaB217xYRXUeIzsdQFk\nrw5gGpq9uoXTM5XC7FmA/Ya1VEOylNpDHf8dcJIkSZIkSS6th0BJkiRJkiQrcNTpYFZJxMK7iGFW\nFtJlFRwslIxhWfwul8/5LdzO0pIsJG/mbf0WHm8VhMa4OvzMttvCB63Sg1XgsjQ8sqpmPO6WVmcp\nYJyH6+VymAJhFVX4uYXu23E/hZQTY/1ipmKRVbmxSk0Wjj4Temzh7lapYQa30ypQWYqKbb+F3++H\nfDKslGlNVu2M4axsz0xLueOOO3bT7Kdsz9xWtnmmp/Fzrpff5bWMfZDpPQxN53dZTY1pZTzHdj7I\nzreloB6rQ1OAiceMaT1WFWumbc+EspNdE9juWF2H7rzzznM/59jC9mXV8NiHmNJkaWu0f/22FHTO\nx21ieDnbNscpu85a27brKdk1eqY9WTUlrpfHy8YA22a71p9atU3bXrtnm/nuzHJmvmupIjPbw/PJ\nNCb2WfYvjkXsX7zP4vXe0tbI0iLtGnXR9+0ezPaZ07y3ZPoJ+y/7+L333rubZiVCHhemVL/2ta/d\nTds9JO9dWR3sda973W6a4z7Hay6f229taCYt8BTGTTOTBmW/Gw69Ttt9qV0vLQXbfm+R/WYiu/Zz\ne/j6Aqbgs4qoHYeLKifbvaL1O6uSaL8lLc2f1y97rQy3h33Enh2wffDegqy6tKUa2nGYudbPjL/6\n3YPmTpIkSZIkyUnqIVCSJEmSJMkKHHU62EVVAJ7FED6GzFnorIXAcV1WIcpCuixckCzMz94Ibukk\nFp5pb7a3NKn9v81sq4VLWpoGQ+n4XR5fhrBamD6Xz+VwfxjCx/n5Ob9rqQLcHoY+H5rOdMosVNP6\nyEzVCEsVspSxQ1PA7DxYGLilOVqKpaWm8jjMXDfIwvXHuDqElf2CYd5sn1wfP2fbZmoV+69VGeP+\nMK2My+S6eNytohXD5rk9rNDG42LpYDSTWjKTonKsZtJzbYzjsbR0AUu74NjE5VgqwEyVIn6X4eHv\ne9/7dtNsg0w9ZDWqK1eu7KZtPGWfYJvl8m0ct2v//v/bsWPaF48F09s4NlnqKacPTe+x6yy/a6nZ\ndi3jcuz+YWYMmKnWdGoViC4z/h9aTXQmnWImbcq+a9h3mNLEdE6mLL/xjW/cTbPtW7ueqaRm6Sf7\nrMKOjRF2LNhHeM/J6qe8ln3VV33Vbpp9xO4trSow+wUrnnI5XBd//zz88MO7aaaMcZy1CsR2P3QK\nYyXxumV4Hni9ZLuyKk9sF5xmauTMvSVxGywVy6pqsk/Z+M7pxx9/fDfNdsFpjrOsNsk+/uEPf/jc\n9e5fT+wVKkzj4ufE/eQ8vL7wXsH6NefhPQHHax539k27Z565hrLP2m8JtkX2TXsNiy1n5l6NigRK\nkiRJkiRZgR4CJUmSJEmSrMBRp4PNpHswPIrh4gynsreDW+gxQ8YYCmipVYe+TZ/ftZBqSyfhMrkv\nFkbL43PXXXdd9TdLt2NoI0Pv7K3m3AerqsDvshoNQwk/+tGP7qYZXmtpZcQ32rMCEcNouQ0WUmnp\nNgzdZ+jgTEj8RSHLx2gmHN0q6LF98viRhTzPVFiwUMeZijcWzmztmtgfrVIh26yljFn1hP12ze/w\nOFqaiqUB2HXEwmUtxYz9gtdTu8bxGsIwWk6zb7L/sn/xc1ZaObQ6naXYnELftAojlvZln1t6CK9h\nDM22kHi21Zm+ZuHobF+sVmZ9h+MG57FKXBbeT1ZlzCrmjeHXER4XbgfXwQp9HFPIUncsNWMmhWom\n7ZbHi8eX6Q3E/bLrJlkKmN3fnELftOM9c69k92/s75YWb33cjiVZqqZNs2/yfu2hhx7aTT/wwAO7\naaY+8PrNvmyVY2cqhVla1RhXHy/e13Gf2TdnKonadZDpNLx2WP+ye2Pb//3XNzyL97dvetObdtOs\nAnbfffedu21cpvVZbrPdb51CdTDbD7LKhzYG2T2bvWpiJjWW7LetpTnzntMqWdvrOpgOxd+8ZClv\n3DYuk31z/7eRpdJZ6hPx/pPV8Xi/wmsBjwv7FO9dOc17TvvNa6/DmLkP4zmz+/OZlC777WQp9zOK\nBEqSJEmSJFmBHgIlSZIkSZKswFGng81UomBKgaUlsYqBhXkztIrhzwzRYmgrw+FmwmstpMvSm7gN\nDP8khrxZaCdDzhkiOoaH2vM4ct0WZsZ5uD+cn+t65JFHdtMf+MAHdtMM3yW+rZ2hejzHDAvksWAo\nLM8Ht4Htg2libB98Yz6rXNjb2k8t5YQsvcb6i6XaWFrDTDqJsQpUtkxLVbP+aP2I7Zd905ZpoaMz\n6aL781mIrFUBnAkZteVbRSVWg7Dzail5Fh5t6WMM92V/5zZcplKYHYdjZcfV+s7M5zzeDIW2VAmG\nY89UnbI2b1V3uC6eZ1ZF4edsF7weMyXcqoZZJUku06qrjOFpTVwfjynn4fHlNvF4kV3jeEwtDXWm\naom1FS7Tqt3YWHyoUx43bb/tOmfXf7tvtJRGfn7o+GupgZzm+b///vt30+9+97vP3R47DhxD2Kes\nKp3db1g62D5L12JbtTZm9+u2b1wmq1iy7/PY8bcH55+ptsj7WEs/YZvg/TD3l79hmBpm90MzleeO\nlY35PE6Wam39gmbmmanmbPdKTFeySmf2W5JtjcshO5+cn32N00wfmzkOY/j1iMedYw378sx9OfsI\nr18zlTTNTBuaOfd2rGd+/8x8136zzSgSKEmSJEmSZAV6CJQkSZIkSbICR5cONlNthqFPDMtieCbD\nymbC4zltodlW1cuWPxO6Zd+1baCZEMeLKngw7M8qeTHEzkJkeW4stJGVfZiKxXB/njNuD9PBmHpm\nVW3sTf3cX6aesSoZt4HLsepFlqZ4yqzdWng2Q1XtDf1k/cjCoi0tgN9lO7UQZrYFq9RhYafcF05z\nOdwvbo+lyVwUTs+2x/5lx4j7YNdKW75VGePxsnPMEGHuG6effPLJ3TTTA/hdpvWyjzN1x0LiLT1p\nZiw5hbB2OjTtZqaCBK+vnOa1kKlYM+kkxPl5Pu06Y22H7YVpVZa6xfZiqRJsd9y2Bx988Nz1juEp\nKzymHC84v6VQWYqHjbnsgwzN5/5bavbM9ZH7zD7L48h+aql9ZOOHVVg8hb45U4nlMqlyNFMRzioE\nWSqhbSfXxXs09ke7HrMf8Xwy1Z5p9OyPxtLB9lNMuM+8x2M/t9SamYpNHGeZtsw+wvU+8cQTu2ke\nlytXruymrZImz5nd31gVJPZN7pctn/3aKh/ToSknN4KlAc2c55nvzlzPbHuMVYuyNCmrSmfVt+z6\nYOviNMcZjj+z+8v5bLzgNlmFaKuwyGPBfmfV1Ph7kK/7sPFx5jpu/cLGWWtPM1XlbOw5dNwsEihJ\nkiRJkmQFegiUJEmSJEmyAj0ESpIkSZIkWYGjfieQvfvH8tuZC2fv1LB32Vj+vJVgtFxBK1U9824V\ne4fFTN65rcve13PR+my53E/bPuY7Mp/00Ucf3U0zX9PKzzJP3N7Hw3m4n1bqlDnpfEcRp5mTau8f\nsneQ2Dsenqt3Anyp2Ls6rA3PvJPAjocdGyunad+1krMzebbW97kNzB9mm2UeMtsm8/+5/TO51Pvb\nMVOS28rJ2nserMTloaUv/3/27j34tvus7/vzxXdJlmzp6H61JMsYG4yxSWAoDXQoHZy4GdKmMw0J\nblOmnXZIaJMmmZYSc5t0kpQkTdKGkBJCKBBaTMOlQ6B1IAlmWqAjuR1ZGBvJut+si2VJBoO9+8fe\nZ/l9tn+fn5519k86e5/1fs2cmaX923vttb7r+/2utZeeZz1sC441Pqfl/vvvn5b5bBnOA3xGRJr3\n07OR0vzOuTU92+AQxmanxGnneTx8D5/7wOfrsA/zGHL+nvsMPPZH5t4T5wqOtfQsLj7Hjcs333zz\ntJzOxexrfFYIX2f78FkIx0nP4kvXNOncn/o/2zedy9Lzhzrnr/ScIbY725TnxzTPpBK76TkbfH5D\np3zuuZbmy0559iS1UyrdnZ6jka6HO9vD93Ms8JqL45rP6eEckp7TyW1O1w/cd/YLPn9n+7qVbZee\nP5e+Oz2jj6/zXPPGN75xWn7/+98/Ld9+++3TMsfUbbfdNi2zjTq/JdJ1FbeHx4nt1XkeWHp2aJqL\nDmFsdp5tSam/zX1Wy9xzdOprvFbi2EnPfaN0rNKzPDlvcNxwG/g6zw+pj2xfW6W+x3bpPBMorYfn\n77SfvLagzu/2znOl0rlv7vOpKF3Hpn6Z+kSy/1fAkiRJkiRJ2pk3gSRJkiRJkhZgr9PBqJMelUKr\nUkpUSgfbJewxpa2lUOEUvju3NB2l8M9t6W8pzCyVsWW4GsNfGT74+OOPH/k624UpNAyXTeWjU3n2\nFNqYSlVzm7lf/K7U5zqlMg8hdJb9NpUwTMe8s5xKelMqN90Jo01jluGlaT38bArvZzgu+wv7JlNd\nmMKYSjuznbfLKjP8letiyHdKh0tpLfxu7k9qu7TONO64TpbJ5b4xjPjBBx88cjt5zFI6GKU5N4Xy\nUifV9lxL6SFpuRMmfOrUqSOXeZyZrsfPdsYm38/+e/3110/LqYQ7Q9B53mCfYqraQw89NC0zTSql\nQ3F7OGa5PamPV/XOx+yrnVLCaZ5NqZdMz2O7cNt4DuVxTeXCU/lcvs42YvvODUHvpFUcgtSWaW5P\n50G+n8spPSil6/G7UgpFStlI6Rcshf6xj31sWmaZc87f7CNcTvuVUgC5v08++eS0zDmB12jbuC6m\nTPI68LLLLpuWef2Zjh/3853vfOe0zPHOY8O5hu3FsZn6RJpn0/VKGjvc/pRmzvmO70kpiIdmbkl7\nXoOk9EFKc3wqf56uTfge9iPO/dR51AmPc6ccO+d+bgNTKjlm+V3pN3VVbjuONX733Gv3NF64Hl4D\n8xhwXKcxklK9dtFJI+xIKasdhzuqJUmSJEmS1OZNIEmSJEmSpAU4mHSwJKWopHSqVOHquCpaR0lh\n3Sm9IKWAMayO7+lUBOo8tf641LAU6tZJB6NU2SVVZ+D+p5A8VqHohLKnJ8AzbYCpAp2n7TMkOvWP\nFPp5aGHtNLciWAqF5XHupPWwD6a2TNuWxjV1wqJTyHNKpbr88sun5RRaTinsf7u/sM9fddVV0zLD\nyxkWz77KFDWm+jC959FHHz1ynQzf5zYwfJ3v53cxjYepXhz7PMas6sT18Djx2KQqbgxlZzumMOjO\n8d4naW7upIOlVAb2C6Z7sF1TtY0UKs9t4BhP6WDsU6kq2W/91m8duQ3s+0wVYR+85pprjtwXjlOm\naKS+tn3e65zjU1pip3oiv5v73Km2mSpsptStlJ7K13lu5TjluNtlHKV5/xCka6J0/ZaOLV9P77nr\nrrum5TvuuGNaZt9OKUrsazxuKRWJx5+vpxR5po3wuo9jjX0nXZem63+eQ1J1zqoz+znbJaW+UEq5\nIm43qyp++Zd/+bScKhOlxz2k+SS1UZpb0u+fVAU4pRJ1KskdwnmzU5E5jbXOb6tOlae0zlQRjP2U\n44jLHIPs49xfjsf0WJL027NzHuB7eF3B697tfsR95vZxvel81Pk9lY53Ouemyotp3KU5If0+SRU/\n02fTNs9NSZt7D2X/R7IkSZIkSZJ25k0gSZIkSZKkBdi7dLAU0kWd9K4UDtlZZwpPTlWwmH6Uqvfw\ne9NnGaqW9iuloaUw6hQKur1NnfBHSmk/KYQxhRumdDCGOW6H/J7G9iKGCzOMmKkCDEFku6QKMamS\nDduK23NoYe3p+Kcwxk6obWrjTohlCntMIckpDLPzfkoVW1K1Ab6ffYR9OYVnsn22Q2eZrsM+nFK3\nGCLMNBCG4997773TMlOxUkUhbhND37ltTMXhPMCKRakSGfcrzRXchk4ljHScUrh7mlv2VSc1jFLV\nKaZmsE+xT1KnakdKp0ipkeyb3Ab2ZfYvpj2x/7LvMMyceD5h/+X3phDsk0x9SFVCU/pZqg7Gakcc\nRyk0n2Okk06SKuKkcZeudTqV5E6qQsq51knZSecgtj2P54c//OFp+ed//uen5TvvvHNaZl9lH/nK\nr/zKaZljKh23dI7mZ7mdHJtM4UzVpdgfuY/pOoufZSo/r+O2r1XTbwNec/O7KVVh5DallNdUjSlV\nFU3jKI3TTvW9VFUwpQKmyl+pTxxCJc2kk3LKc1+q2ty51k3vT3Mh5wGuh/2c/YjnLB5P9h32cR63\n1B/T/vL19J70u2372p79kPuZUjXTceK1CNM/ie3L96eqZhzjaSx0qs2m9L/0+5dS+ltKN0vVFucy\nEkiSJEmSJGkBvAkkSZIkSZK0AHuXDkYpJCqFMXaelp3C6VM4VQrf7qSDpTSuFGKXwrvStqVUtZTa\ndlzVs7lh2yl0jSGMDBFOYX4MGWRoXyf9Km0PvytVK2MIYkoPSaGzqR1T+tCh6VQ6YB9LYaJ8D8NE\nOyHbKTw+pUClMF0ek7QeLneq7KXqW7feeuu0fMMNN0zLDGVnGD9Dc9nXqs5sL34fq5ExLJjzDt/D\n1DB+B6sr8T0MkeX3Er+X0vzIY5wqTHB8pRRWvifNPyklie0zN/X1XON+p5DqlHrJuYrH9uqrr56W\nU5oGx3U6z6ZUCS4zlZB9KqVPsp+ychlToO65555pmecZpoxxfHH93He2LdNbuA3bYe1sl5Q2klLH\n0/FIabHs50znfOihh47cBs5HrI7G9XdS/tim6Xhz/k1pmGmspcpobPcUNr9POmnI6XqBfY99inPz\n+9///mn5/vvvn5Z5PO++++5p+QMf+MC0zEp8N99887ScUq64nbwW4zF56qmnpuWUAsOUYqZnMkWY\nfTOltHAMpZSO7UcCcP7qVOXrpF2k5ZRWx+9K15CpOmmq3pXmh5RSmM6taTntS+q7h3De7DziIF2n\ndyrVpvTB9J4kpf9y7KRKUzyvpd8x6fcTx1FnH5N0jbadwpj6MMd2elQIpceg8P3cpjTeOylgSeex\nNR1zq8p1flfOTQ0zEkiSJEmSJGkBvAkkSZIkSZK0AHudDpZCojqzH6NgAAAgAElEQVShmp2wqbnh\nuwyfY2oRQ9xTeFd6AnpKgWEoXSdUvFMdbDtMLLVpSifoPBmf7cLQYb6ewstTKlaqPJL2he3LkMqU\n6pCqM6TlFG6XKvEcQlh7ksKNU3oB9zWFNqf1pzQLjhGGv6bKTintgOtJ4e4ppS9V8+B6rrjiimn5\nyiuvnJZTKijbbXuMp+9g6g5Txvh+pmulkHK2BZc573Be4+s8BikEPYVTp9RBbmeaZ1KYeieFL1W+\nSRUG90lKs2LbcP/SuY/HMKVgpKpYKZWhky6QzmVpHuW2sYIY0xw5DnieSWkjKdU4pRQfp3PeTOeC\ntMxjxjQ2plezIhrT3timTL1jWk6afymN03S+5nFK80Baf+qjneuwfXJSx5+Y9vihD31oWn7DG94w\nLbP/8xqHY4H9hWmV7F8pbYTSOOVxZnoXty1VZE1VtojnpVSNiHNC1ZnjnOlzbKOUBpN+b1BK46I0\n36V9TtVpO30lrTNVYGV/TXN69xES55t03dFJH0/XI+lxIp2Kz+m6Oh3DdD2YtjnN050qcGmuo+3X\n02/UNAbTdUZqi/TZ9OiAlBY7NzXsxZa2ofMIm9b6Z2+RJEmSJEmSDo43gSRJkiRJkhZgr9PB0hPX\nGQLWCd1K76cU+s7XU8WPlNKVUqYYap3SERjOt1056LRORS86LrStU5Ughd6l9CuGAvN1hvPyu1Ko\neScsNm0Pjw3Twfh62t9U2SFVc+iEex6CNKZSaHAK4U/h/51xymW2N9t4bppdJ40vVblJlZiSlGaS\n0nm2pTTJlNbEEPxUaYn70wl37lRF4XcxjSFVSkrjgtuWQqJThY+5lRQ7fXqfzA3vTe9PVUJSikOn\nOk2nv6R5IIXQ8/iwAtVVV101LTNVkZWsUl/uVALdtbpjGs/8vnQuS5WQHn744Wn50UcfnZaZXs1q\nZ0yfY9U/jtM0Xrj/3AYep1T9NF3HdFIpDqHS0Fxzr1nY33jdlFKhWX3ygQcemJaZJpiqC1F6hALH\nO9OLOQY57liVLqXyp/NP6o9sN6ZXf+EXfuGR21N15jUe24XXhKzWydfTuTXNa2n8poprae7brqL0\nQutP8/Xcc1zn9ZSSsw9pMi9kl0rQaTm1WXpkRadScyelOp2v0++kzrVuZ97tHOezSR9M19xpm1Ja\nXSe1P6X5pXGa2rHTFp33zK32RSl1sNNXkv0fyZIkSZIkSdqZN4EkSZIkSZIWYO/SwTpPX6dO6kpK\nLUkpYAyD5+vcNlZYYNgtw3eJ4dV8D8NRiWF+DAOmlHrWTZXopAGkKgYpNY7huAxZZxul9k3pVylE\nOKWT8Nhwe7ic0ntSJZQUOkjcNr7/0KqDdVImdwkx7oxlthmPbaeKVAq3TNuQ9jdVFiNWI2HYfKpA\nxL7GdW6HbaaUrpQyydf5WW5fSvdIVdnY1qkyHJdT5TOmqqX0yVQ9MaXepWoR3J40d6f1HILUnzuh\n73OX57bN3HB3SmOZfZCpTkwTSymW7FMpHbWTmnqcTppNmv85Trn/nBcefPDBafmhhx468v2sAsZq\nSawWk9IVuJ2cE5iSRGksp/7XSbFIbXgIKScp3D5ds6TjwDZ48sknj1wmnmtYmYvvZ6WwlGKbri25\nPRxrTMtKKZ8phbcjzWnXXnvttMw25Pmtqur222+flnkuYPoc2yud45K5lRc7aUXpkQuUUmdTZTXu\nS6eCcicl6WxSZM8lzmcphYj7zeOWrpsozdlsJ46FdD3C702/T9My18n0TB7/zuME0uNNUlprukZL\n5+6qM89H6dovtXXa53St03n0QXosSSeVLj0eoVPpkL9P+XrqK53PUicFjPb/LCtJkiRJkqSdeRNI\nkiRJkiRpAQ4yHawTlpjSPTqpPAwTY1hWqkCVUsP4/pQa1UnFSNWRUvWiFF66HarG704pHim0jN+X\n0q/4OpcZvp6qD3SOE7eBoZ/chpSqx7brVEfqPFW/EwK+r1L6AsdC6qsphDW1QUr166R3dcLL01Pz\nqVMhIqVupXBshhAzBHfutlWdGSKcKkDw9VRNMFUeoU41hFTxJX2WfaKTupNSATuV2HYZX4eWDja3\nogul82ZK5emEaaf1pPQrvt6pjpVSbDnW0rVBqiTYuR5I6RTb/92pZtL5DvZzVi9iRTC2C6ujXXHF\nFdMy04TS3JqOJc+V/K5UrS2F5af1z61mmirS7ZPOmEpV+dK5gGkTbGNe17DvcCzwWPGaK21nJ82X\n5xamVT3yyCPTMlMVr7vuuiPXz/mE60+VMNkmTAe79NJLp2XuY1XVm9/85mmZlcPYpmm88D3USd1K\n+0mdtNs0d7MfdFIv03fNrfKYzu8pjfCQ7VJheZdKtem3auprKXWey3PTnDuPaKDUB3ke2/69ma73\n0nVsp6+m/p9+J6f0sU5qPXXOTel+Qec3QDoeu1YwPcr+/zqVJEmSJEnSzrwJJEmSJEmStAB7F2+b\nQscZWpVe74SJdaRKYQzpYnoIw6hZkYFhbgzlZbg3X09VkChVE0vpNinEt6r3FPEUvswwXKa6pWpc\n3O7OsUxPTef2cJ18Mj7bN4W4p9D0FKKfUu9SW3dSBg7B3G3vpOakPtkJ7aTUjzppZSkEl9+VKiZw\nPSk9K4WLdsOGUzh3p/JZp2JNCsdP1QBTOk1KHWTIPivKUGq7zrHvVMOiTsWsfZXSZFN1R0rn07nj\nJb1n7rk1jbs073ZSTTupt+xHqd3SuDmualBnHumMKV43PP7440e+zm1lSgurHTFNLKWrpH6Q0qg7\naa6pjeb2j12q0+2TzrVVqmp46tSpafniiy+elu+6665pmcc/VWxLKY3pvNFJUeL2cJzy+uvWW2+d\nltNjEFK/4HmAutW0brrppmmZaWkprZQpYKkiZ6pmm65X01zTOUd35rVOCljn2iCtk1L/OIR0sLnn\n9l2uKdL8Nzc1O6VJsc/zuzjuKM2daex0ron4vSnVmr+xtreBf0vtm6owp+9LjwtIaXKdVKzOIwuS\nuY+rSP2mc82X1pmuPZL9vwKWJEmSJEnSzrwJJEmSJEmStAB7lw6WQpg7KUQpzKpTwSGFVTL8iqFe\nDG175plnpuVUVYHpU0888cS0zBBsbj9DAdP3MkUpVQHj8nHh1Z1QP+4DX2dIItuC4b+p6gxD9fj+\nFBbJ8F2+nylgKd2O35XCphnWy8+mdIsUbtdJadlXqe9RJ5Q4SWGoKRw3tV9KV5mbxpLSUtjf2S9S\n6GyaNzppDdvv6aTGMRQ2hcunY5NSXhnmnVIa07zJNAaG5d9yyy1HbgPbiykNncoZc6uinFQlhXOh\n09+o02adSownlY4zdz2dtK+0/Sm9q5Mq3q3oOHe+S2mPqaIl08F4rcD9ZDoQxx3HUafiCc+hTN3h\nuZIVlFI62C5pgdSZu/ZJ6mNp/1I6UUoHu/nmm6flX/3VX52WP/rRj07LX/zFX3zkd7EvpHGRjlsa\nU0m6TuR5M0mpWuk8k8bT9vt4fkzVNlOqVzo/pvk0fbaTDtZJHZx7PChtG6X0nDQndrb5XJtb7Wtu\nus/c6/r02TQ2eS3GlNyUhpmq7qbjlvpp59o1/WZK1bG335d+x3KZ7+dc1qlamh7lkKprda6Hkk5a\nWafq6txzaKp6NpeRQJIkSZIkSQvgTSBJkiRJkqQF2Lt0MOpUAUuhZCmlKYVS8rv4nhQ+yXBphlGn\nykF8P9OVGDrbeQo9w+1SlSWGETJ8eztkLIUnMtwwpWul/UnVuHj8+B6GM6a0F4bvMiyQ25CqknFf\nUn9iv0kpZunYpPSZFNZ4aNivuE8pPJs6aR3J3Cf3p77cefp+Sg/phJ1SSjfj/qbw820pNDTNa8Tv\nY/9PVUtSqDy3gX04VTG8+uqrp+XbbrttWn7Tm940Lae5m23BOSuNuzRfE49HSos9hJSTuelRnYpa\nlKplpbSyueaGV6eKi2kup1QBj8tpDtk1/S31pZRqTSld/Mknn5yWee67/PLLp2Wma/F8xPNjSrNP\n5+I0djqpRCdVMe6Qq4NRmv9T37vqqqum5be//e3TMvsIjzkryLF/sY900r7StWuaW9LY5HUT18l5\nnX2ZqS4p/aiTWnHc+9K8wOPRqZLJuSldc3RSbjo64yKltFCa39PjJDqpMYeQDtapEN35DZQqUHUq\nCadHX6QqVewvr33ta6dljpeUDsZxlFIY03VWOuemMcH3cF/42+u43z3b4/a0NB+l6/iUTpXSwdL8\n0Kke2/l9nvpTJx0s/X5IVdLYn0wHkyRJkiRJ0rG8CSRJkiRJkrQAe5EOlsKzU3WAFEabUnxSakYK\naUzhogwHY0geQ7EYspu2jeFpDOli6F0Kx2WKFbeTaRxcP0MEt0PRud0p/JHLXC8/yxBAbl96Kjvb\niKHDKXSS7XLxxRcf+X6uM1UESyF/KWyRbZrSKhiymSqbHEI6WCdcNoVGppDh9J5OBZAUSpmquqU0\nqU5IMKWxzH6U3p+2gfvOvszt356LUsh3Ch9NYfQpRZHrSWG+ac7ieximzCpgrFhz6623TsspjZTz\nRgpTTjqpDskhpIPNTQHrVNFKFdVSCkIKhU5h2tT5rlQ5h1JqY9q2FNaeQsg7FUXPRidtgCk9TAfj\nuGO6JVN9Xv/610/LHC/pfJ3GdUrf5jrTNU1nDM5NYzk0c1MmeX3BeZGvv/Wtb52WL7300mn5kUce\nmZbvuOOOaZn9ghXkuH72l3RM0vHhfM+UNPYv9mW68cYbj/xsqmhH6Zy+LVWcTJWW+DrnHc4XKUU2\nzZXpuryTQjV33uG5u1P9Mf1uoZR+wu1nG+6rTvpOZ8yma7m0TuIc2Unf4fawb7IqMo8hqzGn37kp\ndZ59s1MpLKWMpd+Cx13TpgrW/H3H7eu0e6dq+Fyd45SqC6fz/tyqxp05YZdKYUYCSZIkSZIkLYA3\ngSRJkiRJkhZgL9LBkrmVIlIIVQqnSqkG6WnqqcII00bSU88phaOn8LdOCHYKlU9Pqt/+b4aCs2II\n942v87MMSeT7uU1s01RZLT09/5JLLjlyG1KaHJe5DamaFL+X70/hknw9tXs6BoeAxyqlEaRwUEph\nyCmVI41fpizw/QzZZrhsGkfpOKSKJ6k6SaqMkKoQdiqEbM9Fqb91Pp+qZaVxkcJQie3I7eHYZLrK\nDTfcMC2/4Q1vmJY51u6///5pmSkEKS12FykE/CTTfl4sKTy7UyGrUwVvFy92Kk+qhpH6b2cMpnkp\npZCfjZQCx+3jefPRRx+dllkRjNvKNB6mIafxkkLT51bBSXN06k8pTaaTAnYI43GutK8pnZMpEZdd\ndtmRn2UfYT/6oi/6ommZKTvpOi6lsxPHDlOsbr755mn5gQcemJZ5nuG5gulsqSJY6iOdFNftbU0V\nMFOlyJT2RWmbUkXLlAbfSaPtSGOts/40V3YqAx5adbCOTqpfWifbKV3fpt99xOPAMcJlVgTjWCOm\nkqXtTKmQ6fEFXOZ44m+449INUyW+tJ/8Dkp9Oz0Ohuam8+0ipYN1qu91cB93SWU3EkiSJEmSJGkB\nvAkkSZIkSZK0AHuRDtapLpTC1eZWLKIU6pjSwVI4J5+Inp6OnlK6Uph6+l6G/6V1dtI7tj/DUPtO\nOliq9sX953cz7JhSmhDbLlVp4ntSOltKB+tUTOC+MN0otQOlJ9UfgjS+qFOdh22ZquxRCmlMFTB4\nfBgiy/UzZLKTlsV+lCr98bNsh1Rph/vCbeB+bUvVUFLqWpq/Oukh1Jmz+B7uw6lTp6Zlhv6zEgzH\nL0N/GVKcKlJQJ6S2cw44hMpEqY8xFS+lfaXqP+n4p76aUqHZz9nvUih3Om4pLYP7wvRBpsOkMPWU\n7pDGVqoatC3NL2yX9Hn2//vuu+/IZab3MDXozW9+87TMdEuevzgnckzx/MXjyjRMfpbv4fhN1UY7\nVXY6Yzal0e2rlEJFnSp7qboU2/uhhx6alu+6665pmf3/2muvnZY5Nllxjn2B83SnQhDnHFZ95Dhi\nahjPD0wfY2pjJ02wU7GoKp/7UgVMfn5umjBT41L10JRilvrN3EpGnfk0pcsmqa0PIQWM0m+fNMdw\nmf2F55RUqZbjNFXBStdfXOY2cJxy3HEsp99t6REK1KkOxrmF/Z3b1nmkx/Z/p8cudObTpJPuntbf\nqQI297vSe9L81anu1xnLc39v7v9ZVpIkSZIkSTvzJpAkSZIkSdIC7EU6WEdKj2KoFMOj+J4ULp6e\n6M+QsfRZhoGnp8d30tZSyGoK1etUNEspHcdJqWhpXZ1wWe4Dq1xwnSn0v1PdLYX1p/DitF/chpQa\n1nkSezrGhxDWzrZk2CdDPTkWHn744WmZqQ/pifgMHb/yyiunZYatcpnrueWWW6blBx98cFq+4447\npmUen9tuu+3I9Vx//fXTcqqcwjD7D37wg9Myw1zTnPPII49My0xPY5gu951jYnucMvT/3nvvnZaZ\nKsJ+y9B/tkUKfWe/ZVtwmxh2/PGPf/zI7+J6GLLMFBJKlS24X2wv9q00TlMqXErhYzvMDb8/F3g8\n036nFMXOOYjHkHNeOoem/t9JI0jpmSlsnnMCx8TTTz89LTM0nduZzgPp3MLzVUp/215XOt9zXezP\n7P8c148//vi0zDa66qqrpuUrrrjiyG3i2Eyp0NwfbhvHHduax4MpaZwrOue1zvkxvefQ0k+STopT\nqtjGz7LPM+Xq7W9/+7TMY8Vjy/ezgtjc9LSUJvi2t71tWmaqIvsyU8BS+kzSSRetyueC9IiHZO6j\nJdJ8cVL9OT3iIa2f7Z6ujdPvqM7jAA4hjbqz7SlNh/0zpe3zERccC2k9PDclKRU0neOYwpuupYn7\nkn4PpbTulC6d0qO3t6FzPLgd6VokpXBSSv9MOqmDadxR+p3I3w/pkQ6pQl96pEOqnGp1MEmSJEmS\nJH0ebwJJkiRJkiQtwN6lg6VUm1QJJ4Vx8f0MkWWqS6p+wtA7fpbpCwx1Ywg2Q+a4LynMj/vC8EIu\np7C9VDUopTpth7ClUD+GoLN6BNuObcHPpqoPTDNJUjh62h+m8bCNePzSMUjtmFIKO5Wudk3J2xcM\nR2d4I7GdUkgtw1Z5TJI0Htm/2GcZAslQeaZWsM9ymeGsPD5M12AFIo5xtgnfz3Qw7nvqv3wP27zq\nzHQ7psA99thj0zLD/bkupocw/Yz9NqW5pgqIqbJeClXl/vA4cTmlOXEe6KRrpfSeTtWGQ0jVpJSK\n1Ek/TWHwnWqSKZUhpULz2PJcwfB1nlvZNznWHn300WmZYzyFirMvc52pL6T2TP19Wwqv5+e5P0zL\nYbol2yWlJHN+4bmPYyRVCeU+cO5LaQOd1GnuO4/33Gp9naphhyClKaXri9QneQzZxvfcc8+R6+R7\nPvKRj0zLrKbH/jK3ahK3mediYp9N5+jUd1JbpbQkrn+7CmGn6mVq306Fzc53pfMp19mp1EnpeKf3\npFTDVK2M25zODenxGYcgpZ+m6wvua0p94ljg7zXOhal/pRS9lHqZUhi5/Xx0Q6r6N7dSXErZTuf9\n41LSUr/ld7Cteb2exmO67kn3EVKF1NTnO7/j0vak96TfoWme6ZwndnFYI1mSJEmSJElnxZtAkiRJ\nkiRJC7AX6WAMS2PoOENJU0hbJ6wupTSlEMEU5seQWn6WqS4M6eL7U6Us4hPmGSqfwnRTSHwnRH37\nb2wjVmO6/PLLp2XuG0PcU5gct5Xr4f6kNBCGY3I5PUGdx+DUqVPTcgpl5r6kqlT8bDreqepEqmZx\nCFLFgRRGzjZjG3DspDDUFD7JsFD2Z77O45mqMKTPcqzxeHKcpipIKdWDqWFpPKZ+vR3uy7mPx4Ov\nc35JKTepQlBKh+P+8Nh3UroYjvzUU09NyylFNs3XfJ37lSomdKohdNKoDtncNJp03kzpRJTS9fj+\nlKrJ9K50DuF77rvvviNfT3N/GsvEz3aqAG33kTTnp3MKxyDHJtM5ucx5hKmdd95557TMfUvXQ3wP\nj1OqIJXSGDhXpP1NfSiln9DcykT7JKV7pHNcCv9Pqf3ENGf2i9tvv31a5lhjOjMry3Xmv05acErv\nYD/qpLqkz6axeVx1r06KRKeKYWcuSPPjXJ3zV+oTKTWb5vaz1Cadqkz7hNcOnINTZbrOtSXndbZB\nSm1OjzXopPSl9ElWYeU+8rP8nctt4zHketL2cDylKrKs2pkqbFad+aiQa6+9dlrm3JSqkKa+ynmW\ny/ztkapqpkdUzK2Ix+9NjzjgMo9NqujHvkhcT9rO7UdLvJD9H8mSJEmSJEnamTeBJEmSJEmSFmDv\n8lRS+H96wnenmgnD2BiKlaqBpDA8hqqlcPQUHszPMhUlhfBxnQzvSiHYKVSTIWzbobL8PEMDua6U\nlsawP35feoI8QwG5HQyBY3oA24vtkqousb1S6g73kduWKq3wdUppZandDyF0lv2Q/S2Ftc+t4tF5\nin8ag+yDTDNif2SYbgojZYgl+1FKVUvtwNfZZ5kOxXBZfhfHQUorqcqpXin8m22R0mhTVQK2e6qy\nxpBfpuWwwhHTWLjM45dCVVOaSapgkVJIUl98McL4Xyqd6l2U0pw7bdOZqzoVqPge9mVWx2Jf4/hl\nFSymg3HsczsZ+s1zRarW1Em/OO49af/TuYNjhOOI+5PSxzin3H333dNy51zDccdzH3H9qQJRSnNO\nfStVJE3bObfK3T7ppHXMnW/SfvNYsU+xvVO6cEpzTueTToWcTgpquh5M59+5qZrbOilRlM6Jcyvc\npfVTeqxDWmcaX3NTfzvblq4lUmrTIeBY6KTjpIpgfJ3XROm3BR+tQSk1rDMXcptZvZnbz2tLLl99\n9dXTcqqinPoIt4e/W9lHWMmWbb7t+uuvn5ZvuummaZltx+3gMUupW3wPr5OZAsa5L/3G7JyzKD3S\nJD3ehK93Ur0oXWOkY5YqOif7f5aVJEmSJEnSzrwJJEmSJEmStAB7Ed/HcCeGa6VwvhSilcJuU3g8\n189QMoZTpXQEhmKlMEmGsKVqQSkEO1WUSPvIbeYy93E7xJ3vS1WdUmUfSiFqbBeG/HE9PK4pdYXh\nc6lyAd+fKqdwPXw/lxnCx/ZJ4YLEdkgVx/ZVqp5EKUQ8pQOy76QxzpDM1K6pqgK3mf2IaSZ8PaWe\nEcN9WTmHofh8nWH5jz322LTMkOCUisGxyXVu7wO3KaWQ8PPcDlaIYdsxtJe4nrQNqQoYX+d8ytSw\nVC2GY5bbmY5TSg9OczHX0+nr+2puKkCnwkZq+/T+NN47FX9Y1YjLDClnn+J72DdZbZJ9eZd0sNQv\nttuc+8MxyMpfnBeY0nbPPfdMyxxTXA/xnMK2SP0gpSdxPHI5VbtJofKpClgKoe9UWUppYnP7+rmQ\nUoiO6z9HSfvN48llHkMeH/YXXn9ed911R64npSjNrcyV0s06qWR8T7oG7lbum9vWXE6V7NKxTNei\nnepCKe0nndfSdqbjkea7zjFObZjG+76au42p0hSvV5limX67darPddJe02/AdI3N45YeUZKW2Y9S\nO6Q+xXNuqr5ddWZ6cqo6l36XdXTOKekYpGuF9Luvk9rJ9u1UIJ6btkm7PH5k/0eyJEmSJEmSduZN\nIEmSJEmSpAXYu3QwhlQzDC89NTyFjHGdKXyb6yeGPaZqZQnDshhSPbfaQAq9S+F5qSoEMQyt6sy2\nTukSqUIbpWpqaXluigeltLdUESztSwqp5TrZ51KKWeor6an1hyCFZKfwSUopPmwDpiul0EuOzdTe\nPLYpNSqNBaYG8lix8hXXk6qPsd8x7SmFELNfs0225yJuBysK8TtSZR+m1vz2b//2tMzjx/QAhqpy\nP1OFMr6H+8/3cG5J4a+U5opOaHoK5U3h96na0flobqWwFC6dxl2nyhjnVPYRpiqyr3E5pdUyZZnp\nYBzX3M5UMaNTKWw75YKf4bhlP+QY5PhlClg6L6Q+yX1IYeSp8ibHaTrHcR5g+6YUsxQST530k1SZ\nqBMev09SmmSngm1qS66HacU8Vtdcc820zLHDCkHveMc7pmWOkdSn0rm+c0zStVLqC+mYd9Kztsdm\n59EJSSfVK1UM7aT9pLkmzbOpYmbnURQpDX6X9BP2lUNIB6PUF9Ix4bzOay7O2elRE52KwZ3ffem3\nZKqcnKq6dX5XdR6lQql6b7rerjozdS1Vs+707aTzW4VzUxpfc48N189jwzmabdSp2EudeWOX69jD\nGsmSJEmSJEk6K94EkiRJkiRJWoC9SwdjZRCG4TEUkSFXKbSKYeRMlSKGo/P9Kdw7hVimkNokhd6l\nqlkptajztH7u+3Z4Xarmk0JYU0g5t4/7xuVOlaaU9sMQQ7Yvt5/v4T6nsL3OMUhtmtILU5ob+/Eh\n4PjivqYqTCn0kuGfPP4f/ehHp2WmRzBUkxV1HnnkkWmZKVrsjylFNIXWc7/Yj5iqlipxMcyTWE2L\n6VwMFeY2HDdHMYWEVYE4H3GZfY+f/fCHPzwtM22GqQVsI6Zxsa3TPJhSUtkW6RhwPZ3Q1k4aaapg\nd8hpX2lO7aQysV3TceM5NL0nhbhzDPL481il8wbnRY61lOp32WWXTctXX331tJzSwVIYfKcNmQ7F\nSn9VZ86PKZWd/Z/nJrYjx2Dqn5xrUqpImn/5WS6zjbhvXCerr6WKa6lPdCqYpNSjuVW1zrW0T510\nvSS13y233DItf8VXfMW0/CVf8iXTMvvmFVdcMS1fddVV03JKmaRO6kqqGpfSDTvfldKeUlrZtvR9\nnXZP1w3ps2lbk8460/hKbZf6Vmc8drY/bUPn3HMIUop0SnNP1yy81qVO5TfqpD1yPqZUKY77kq6B\n03zcqVDH6/ZUEXj7uzsVSdM5LklVDNNjWfie1HYd6TcPz7npGqVTsZo6VeXmMhJIkiRJkiRpAbwJ\nJEmSJEmStAB7kQ7GcLUnnnjiyGWmbDB8jKFeDDFL4XwMWWdoH8OyKFXwYOgl0zo6FRBSGG1KXeI2\nMMycn+V+8f0p1K7qzDZNIbz8DN+TUrqIIXbpqfGpiluqaHMq+8YAACAASURBVMAUlfTUfh5X4nFK\nIdEpjYHbyW1IVWe4nlSFbl+lPpzSEVLlAlYn4fFhqhdDJtnXmCaW0qF4fCiFUjIkM6Vb8ntTigpT\nKPg6xyZT2NiGKb1jOx2M29FJgePr/Oy999575PvTHMT1cB94DJj2wnHB+YTbwH3j/nP8cnv4fi6n\nuTKlD6VqGYemU6GROul0PM4ppZX9lulRXObxT/MlpdRIziepqsb1118/Ld94443TMtNeUqXHdI5K\nc8iVV145LW+356lTp6bllBrWSTPj/Jj6dgqvTxXueMzY1my7lN7FtuYcx7S1TioRdVLA0nXPIeik\n3XSqVKU24PKXfdmXTcs8l7GvpoqmHGvsU52UkLRf6T2p4menAhGX2cfT3HVcGtbcdIk0BudW9k3t\n0qnMlNJskpRKSSnVJbVPJ8XxEFI1qVPxidJ1L9/PMdVJge2kg6XvTalnneumNKZSOm8nHYw6VUe3\nse1SCnfn+9I+pIrV6dEEaRx1jmv6vcxzPa+9O6lwqTJipxrr3KqIRgJJkiRJkiQtgDeBJEmSJEmS\nFmAvYm8Z9sYUDC6nkEmGxnGZIWMMd09PHE9PEGe4LD/L7WFaQ+cp6yn9gtvJZabSsHpRSnfgvhyX\nBsHUmhSGx1C3Trh/Cs9LIXadFI/0vWyjlBqTQugZktcJO+RneTy4ntTnUnW6fZVCVfk69ztVLuB7\nmNqZKmDwuPE9TEdI2M9T32FKYqo+l8Y7t4f7mNJLmcLFz6a0U/aX7c93Kpvw2KS25r5xruR8keaa\nlJLGNuJ7Hn744WmZ8yP7BLeHKUBMN+OckOay1IdSqs5JVVU4Fzph+Olcw/ZL6ScpzZOpS+z/6VzB\ndXI9TDni61wnw6hZBezmm2+elq+55poj358qhKZ2S23F6ljb6RT8G9Ny2PfYz9m3uZ9sC0rnSr7O\ntkvnUG73tddeOy2nOYhzAreN7cvP8tizrdO1wdw0k0NIOUnnMupUdiK2H/sRq4OxUh7PDylVN/Wj\nlFrSSTvg93aqVHXSoTpVeo5LGUzzfKoOmNaVHneQrl3TdS91Hv3AOZrvTynPnHPmVp7rVPfrVPbc\nV7vMH+l3Yuqfc6tCp/WkMcu2T/039SNuG+eTdL5mn0opVp3qwNttkq4J2BZpjKRxnfZh7nrSvNZJ\nv0/7xfNmmk9SO/D4pZT1lNpnOpgkSZIkSZI+jzeBJEmSJEmSFuAlTQdLoVUMd3ryySenZaY1ME2B\n4V0MT2YIM9/DlI0UgpvC/1IINjENglJ6E/clhf5ymWlbDz744JHr535xO48LneV2pNDWFP6awupS\nylxKyUupCMR0HR7LlPZDqfpFCm1MVdxSeGWqYJHCsvcVj2En7S+FUqbqP6wcxbD2lMbFMcVxnSrx\npSoJPA4pfJIYHs8UGPajFObJtmKaSAq5Z0j1dsog24XpcNyH7RSy01JqK9Nr+X3cH7Z7GncpnYDz\ndapwlMLdeYx5zIhtmsZsmrsPOQWM0jl0bpg/pfew6hb7GvsL35NCqvn+N7zhDUe+n32T/Z2Vj5gC\nlvoCj3knLSmlsTDlazsdlSldHGuc43j+ZuoOxwJfT2mbnItTal86rnx/ShFnO3KbU1pvCmtPbZqk\nNLx0XtlXnX3tpESl9/C4pTHFOZvHNlV87aZvvJB0zDnuUnpfpxJX6iMpvaMqX8ewLdK5o3OdluYa\n7n/ah9S30zV6JxUlSRXUUj9L5w/uC9NaD+ERB+lckNIVKaVipYpS6bdeStGbW1kuvSddT6XvSuvv\nVPVKn2W/4DXt9nr4vtR2abs70ljjetKjDFLqZZLaKD0ag3gtlaqDpbZO6W+72P+zrCRJkiRJknbm\nTSBJkiRJkqQF2LvqYKwkw2WGizN0i+HVKT2CIWApbK9TeYapLnyd25lCyRiOmNKwUooS08Eef/zx\naTmF0abKTdvhrim1KlW86IQnpqfMM8WDbc1t5bEkpqUw5D6lYlFKY0nbnJ7gn8IuU2Ui9olOyPi5\nlsZFp7IJwzxTehfXw9SfFILNMZL6aZIqs6VQcfavFBbLcPJO2Hgag8RtePrpp8/4W0o5I6Zy8D1M\n0WE6Acdgpyoh8Rhz/aniHvtHartUiaqTwpnCfVOI8yGkZCapmmSqYpHGbAqDT2kNrMzF48N5mu/h\nengMGRbNCl+XXnrptMz5kutPVcP4njQHz03L4HuYAnbc/M195nmafZX7ye3md6S0n5TmnCpXpjmO\nuH62L+fudC7m/MD1cH5IKeQp5L6TGrSvOucjmlvFJZ1D0/VLmv9SCn4n/STpVArrpIZ1vpfb3K3e\n03n8RNrWTjpG6p8pxS7N40mnjVI6a0obSWksnfSkQ0vVnHvOT8c5PaajU1GqM+ela5m0/s7vrZRS\nnHQecZH6I88V/N5t6fdzkq5ROulj6Rw0tyLY3HNQuqZN6+H7O5URO7/HrQ4mSZIkSZKkz+NNIEmS\nJEmSpAXYu3QwVjrgMtMlGN7GEGw+mTxVC6JOSHWqusXQaW5bJ22EqS6UQgHZDqlyTqrQlcJoq3Ll\nkVRNID0ZP4XIMnScaVwMC0xpIMT1MC0wVWvjvvCzKVUgYdsx5DH1s7S/nTC/fdIJ9U1VI1JlBKaA\n8bilMM9OpYMU5srxntI82deYDpaqiBDf36n8knDcbKeD8TtSKukb3/jGI7+bywxfZuVF9k/251RZ\nkMc4VVfhuE7pZtwX9gmmFXG8pLmC6+9Umkhz8S4VWF4q3FceKy530hpSah1f53hhqlCqTNWpeNNJ\nEWXKVDqXcT3sC53Q71Qphsu7VqlKKRjcB46jlD6Zzq0p3Y7SPsxNaWFbc/vTHN05x6VtSw4hHezF\n1kmDYD/i+zk/pMqYnRT/TkXCTgpF5/XOetL3HifNiZ0UnU7qSmdMUUrhm1uZKP1mSPNaSslL54Y0\nLx3C2EzbOPecn+byVH0vpYDNnfM6lS4pnbM6575OBblO1bB0vV2V0xLTeOxUd+2kxlHnESLpuzrV\nsSk9ZoTzdRq/c+ffzr4nRgJJkiRJkiQtgDeBJEmSJEmSFmAv0sEY7sR0J6Z6pZSNFPqUQixTOFyq\nzMXlFP7H1AqmoqQwrpRORNw2poPxs0na/u2QwhQOl0JDOxWP+B1MA0mViTphdWxT9oMU5pq2gW3X\nCc1MIXapukTqr8eFSO6jTth2CjfupN2kcdEJ003hqalSTQqVT1XDUogsl5nGlMLA076k8GCm3lSd\nOea5Lm53JySbFZXYLkyrTNWI+HpKy+H+c6wxbY2vp/QxbieX0/HrhD6nEOf0/n2VUow7lSVT1b+U\nlpVSfNg/U59P60+pYWmbOyld6RqgU12K25bSqo5LmUlVflLKXDpv7lItK7UddaodpaowKdW6U3Gq\nc11Bnb54CDrbm67H2A87aUnp+oX9jsvpuHVSw9L5Pc2dnXm3835Kr29vQxrPKW0kvSdtU+d6tdOO\nSToe6bNpe9I5Oo3NdD2f+s0hVAebe72afkt25to0ptJxS+tJc39nTk3pQTz/pGuGTuW+XY95+h2e\n9pPHo1MZNqVQ8T2cH9L7U/9Iv+M6/YbSb4+0j2n9J1X9dv9HsiRJkiRJknbmTSBJkiRJkqQF2It0\nMIYyMaUmpfKktCymIKSw4k76WApfT2lATP3hNiedJ4VzfzspTSn8nmGK2/veqcCUQtRSyByPDbc7\npbF0Qi3ZvuwfKVwwpbSkNDRKlXJS2CjbhPuewpIPQQp/TqGXKcWJn03pDp1UsqRTSSGlGnTSDlJY\ndydEtlOZgp9lilnVmekYafyn9uKcwhSwlH6W2iulgKXqXY8//vi0zHHKsc/t5zpf//rXH7ltaTtT\n+3ZCgg9Np8JXmvMppVxRCjtP6QWpCmdKa0jpYCmlq1PxplPZMl0DnE11oJQazO3jMeB47GxT6red\n0HHqhM2n810nvYvv76T7Jid1PjjXOnNMasu57dcZO2n9lM6JKa02SWlfnWo8L4VU8WrueaEz1jpp\nM6ktOm2dvrdT7Whutao0Jx7CIw7S9XineibHFH9/pPNvJw0xVVmklDKV5uZ0TNI82kmf6qQ38bqP\n13rHzdnpb9xnnis757h0LHn9nPYtHYOUMtap3tWpppaupVL/S+f9dIxnzyGz3i1JkiRJkqSD5E0g\nSZIkSZKkBdiLdDDqVP5K5oZ6UQqZ61SySuHhlN7P8DdKlVZSektK6WBo2Pa2dUJh+fmUcsP1ptSP\ntH1cTqlunT6R1pkqsKTQxE7KW1r/+SJVN+hUDUv9JaWQdNLmUghz+l6G76Y0yZReOjc1rHP8U7ux\nL29X49muFnbU96WQ0RQ6/LrXve7I9aexwM9y+zhn8fULLrhgWuYxSNX9uM38LLeN38X1UErtS3P6\noaWJpTSoNOelfpGqhKRUpDTPpXQzSqHQHHe7HKtOFTDqpI5200VTOlmaa1Kac6daSkqH66Qzp7my\nk1ZHKcWbcyLHZmdOT9+b2uTSSy99wXUegs41SJKqPKX+NbdqzdzUlU7qfDq3dioonY1OKkS6bkjb\ntw/SdSmlVJRdUjVTXzmEdLA0Tycp1YbrSWm+qY3TeWRuRV1+b6ow2UnT7qQGdvaF131prGxfA3Qq\ntDLNLKUkd9Kg0rGcmy7bSf9MxyCd0zl2Ouf3VIXzpOZNI4EkSZIkSZIWwJtAkiRJkiRJC7B36WCd\nsL25oeAnpROOvYt9C0E9Gy9GdY9OJRv2g7kVEE5KqijzUm7DSetUW0ohjSl8PUlpjJ3wT4bspuqB\nqY+kcZ2OZwrH7YTNU7daSkrrSP0/fXeq2NTZ1hSOnCqRpdDc9L1p/dRJ1UttmsKyD2HO7aR9pcqV\nnRD0tJ70/vR6Gpud9JDOMe/Mo53xTmkeS9tQ1UsN7lRhmZtWzO9N1TbTGE/b1kmjZgoYK7Byv1Ia\neNLpQ4dWHaxTgYtSxaJOuiXNncvT2Ox8ljrzeko/eSlScjv7T2lO7JwTd9GpFDa3Mibt0tbpXHkI\n582OTrpPp0oVr4M6j76YWykv9c3O2J+bLp3SsNLc3K28l86JnQqgnTHSqZSXzH30zNxzU+faJV3n\n77LOjsP9dSpJkiRJkqQ2bwJJkiRJkiQtwDgfqxtJkiRJkiTpTEYCSZIkSZIkLYA3gSRJkiRJkhbA\nm0CSJEmSJEkL4E0gSZIkSZKkBfAmkCRJkiRJ0gJ4E0iSJEmSJGkBvAkkSZIkSZK0AN4EkiRJkiRJ\nWgBvAkmSJEmSJC2AN4EkSZIkSZIWwJtAkiRJkiRJC+BNIEmSJEmSpAXwJpAkSZIkSdICeBNIkiRJ\nkiRpAbwJJEmSJEmStADeBJIkSZIkSVoAbwJJkiRJkiQtgDeBJEmSJEmSFsCbQJIkSZIkSQvgTSBJ\nkiRJkqQF8CaQJEmSJEnSAngTSJIkSZIkaQG8CSRJkiRJkrQA3gSSJEmSJElaAG8CSZIkSZIkLYA3\ngSRJkiRJkhbAm0CSJEmSJEkL4E0gSZIkSZKkBfAmkCRJkiRJ0gJ4E0iSJEmSJGkBvAkkSZIkSZK0\nAN4EkiRJkiRJWgBvAkmSJEmSJC2AN4EkSZIkSZIWwJtAkiRJkiRJC+BNIEmSJEmSpAXwJpAkSZIk\nSdICeBNIkiRJkiRpAbwJJEmSJEmStADeBJIkSZIkSVoAbwJJkiRJkiQtgDeBJEmSJEmSFsCbQJIk\nSZIkSQvgTSBJkiRJkqQF8CaQJEmSJEnSAngTSJIkSZIkaQG8CSRJkiRJkrQA3gSSJEmSJElaAG8C\nSZIkSZIkLYA3gSRJkiRJkhbAm0CSJEmSJEkL4E0gSZIkSZKkBfAmkCRJkiRJ0gJ4E0iSJEmSJGkB\nvAkkSZIkSZK0AN4EkiRJkiRJWgBvAkmSJEmSJC2AN4EkSZIkSZIWwJtAkiRJkiRJC+BNIEmSJEmS\npAXwJpAkSZIkSdICeBNIkiRJkiRpAbwJJEmSJEmStADeBJIkSZIkSVoAbwJJkiRJkiQtgDeBJEmS\nJEmSFsCbQJIkSZIkSQvgTSBJkiRJkqQF8CaQJEmSJEnSAngTSJIkSZIkaQG8CSRJkiRJkrQA3gSS\nJEmSJElaAG8CSZIkSZIkLYA3gSRJkiRJkhbAm0CSJEmSJEkL4E0gSZIkSZKkBfAmkCRJkiRJ0gJ4\nE0iSJEmSJGkBvAkkSZIkSZK0AN4EkiRJkiRJWgBvAkmSJEmSJC2AN4EkSZIkSZIWwJtAkiRJkiRJ\nC+BNIEmSJEmSpAXwJpAkSZIkSdICeBNIkiRJkiRpAbwJJEmSJEmStADeBJIkSZIkSVoAbwJJkiRJ\nkiQtgDeBJEmSJEmSFsCbQJIkSZIkSQvgTSBJkiRJkqQF8CaQJEmSJEnSAngTSJIkSZIkaQG8CSRJ\nkiRJkrQA3gSSJEmSJElaAG8CSZIkSZIkLYA3gSRJkiRJkhbAm0CSJEmSJEkL4E0gSZIkSZKkBfAm\nkCRJkiRJ0gJ4E0iSJEmSJGkBvAkkSZIkSZK0AN4EkiRJkiRJWgBvAkmSJEmSJC2AN4EkSZIkSZIW\nwJtAkiRJkiRJC+BNIEmSJEmSpAXwJpAkSZIkSdICeBNIkiRJkiRpAbwJJEmSJEmStADeBJIkSZIk\nSVoAbwJJkiRJkiQtgDeBJEmSJEmSFsCbQJIkSZIkSQvgTSBJkiRJkqQF8CaQJEmSJEnSAngTSJIk\nSZIkaQG8CSRJkiRJkrQA3gSSJEmSJElaAG8CSZIkSZIkLYA3gSRJkiRJkhbAm0CSJEmSJEkL4E0g\nSZIkSZKkBfAmkCRJkiRJ0gJ4E0iSJEmSJGkBvAkkSZIkSZK0AN4EkiRJkiRJWgBvAkmSJEmSJC2A\nN4EkSZIkSZIWwJtAkiRJkiRJC+BNIEmSJEmSpAXwJpAkSZIkSdICeBNIkiRJkiRpAbwJJEmSJEmS\ntADeBJIkSZIkSVoAbwJJkiRJkiQtgDeBJEmSJEmSFsCbQJIkSZIkSQvgTSBJkiRJkqQF8CaQJEmS\nJEnSAngTSJIkSZIkaQG8CSRJkiRJkrQA3gSSJEmSJElaAG8CSZIkSZIkLYA3gSRJkiRJkhbAm0CS\nJEmSJEkL4E0gSZIkSZKkBfAmkCRJkiRJ0gJ4E0iSJEmSJGkBvAkkSZIkSZK0AN4EkiRJkiRJWgBv\nAkmSJEmSJC2AN4EkSZIkSZIWwJtAkiRJkiRJC+BNIEmSJEmSpAXwJpAkSZIkSdICeBNIkiRJkiRp\nAbwJJEmSJEmStADeBJIkSZIkSVoAbwJJkiRJkiQtgDeBJEmSJEmSFsCbQJIkSZIkSQvgTSBJkiRJ\nkqQF8CaQJEmSJEnSAngTSJIkSZIkaQG8CSRJkiRJkrQA3gSSJEmSJElaAG8CSZIkSZIkLYA3gSRJ\nkiRJkhbAm0CSJEmSJEkL4E0gSZIkSZKkBfAmkCRJkiRJ0gJ4E0iSJEmSJGkBvAkkSZIkSZK0AN4E\nkiRJkiRJWgBvAu2BMcarxhg/OMa4d4zxyTHGHWOMb9j87SvGGP/HGOPJMcbjY4z/dYxxNT77tWOM\nXxpjfGKM8bEj1n3T5u/PjzF+c4zxdfjbGGN8+xjjvjHGM2OMfzLGuHjmtv/SZrueGWN8cIzxR3do\nCmmvjDG+dYzxG2OM3x1j/CO8/soxxk+OMT42xliNMb5m63OvGmN8/xjj0c3Y/dkxxrX4exw3JzEu\nN+v5tjHGPWOM58YYd40xbju7VpDOvWPG4k2bMfgs/n0H/v66McYPjzEe2/z7zq31HjcW/+ut9X5q\njPHZMcapGdt93Pr/8BjjV8YYT48xHhlj/E9jjNeebRtJ58IO58l4/TrGuGFr7D27Wcefx2f/v83Y\neWKM8b/xHDtz+//QZt3fu/X6nxjr6/Lnxhj/dIxx6dmsXzpXdhib/8UY4+7NeeuhMcbfHGO8fPO3\nK8YYP755/RNjjA+MMf4gPvs1m/Mkx+57Zm73xzbn29Of/8Wtvzs2T4A3gfbDy6vq/qr6Q1V1SVX9\nN1X1v4wxbqqq11fVD1TVTVV1Y1V9sqp+CJ99rqr+YVX9hbDuH6+q26vqsqr69qr6yTHG5Zu/fXNV\n/amq+qqquqaqXlNVf2fmtv/nVXXdarW6uKr+46r6nwduUkkH7qGq+t5aj7Ftv1JVf7KqHjnib99W\nVV9ZVV9S67H1VJ05to4bNzuPyzHGt1TVf1RVf7iqLqqqP1JVH5+zDmnPHDcWq6pet1qtLtr8+x68\n/jer6oJan0P/QFX9qTHGf4i/x7G4Wq3+CtZ5UVX91ar65dVqNWcsHTfWL9ns0zVV9eaquraq/vqM\ndUv74GzPk/H6dbVa3bc19r64qj5bVe/bvOVDVfWuWl8jX1NVH6mqvzd3w8cYr6iq/76q/u+t199S\nVX+/1ufiK6vq+ar6H+euXzrHznZs/kxVffnmvPXWqnpbVf3Zzd8uqqpfr6p3VNWlVfXDVfW/jzEu\n4vdy/K5Wqx8+i21/Nz7/9adfdGyenJef6w1Q1Wq1eq6qvhMv/dwY456qesdqtXof3zvG+LtV9S/w\n2V+rql8biPDBe2+rqi+rqq9frVafqqr3jTG+rar+nar6/qp6d1X9w9Vqdf/m/X+1qv75GOM/Xa1W\nzze3/YP8z6p6RVVdX1UPdz4v7bPVavVTVVVjjHdW1XV4/dNV9bc2f/vMER99Q1X9wmq1enTznp+o\nqr+Bzx83bnYal2OML6iq91bVf7BarT60efm3Wzss7ak0FhveXVXv2oydj40xfrCq/nRt/mdK9xw2\nxhi1vkH7XTO3O65/tVr9GP72/BjjH8xdv3Sune158rjr1yN8c1X9y9Vq9bHNZx89/Yf10KzPVNWt\nZ7H5f76qfrGqrth6/Zuq6mdXq9W/3HzHd1TVXWOM165Wq0+exfdIL7kdxiavGUetb8Deuvnb3YXr\n2ar6gTHGf1dVb6qq/+eEd+Eojs0TYiTQHhpjXFlVt1XVnUf8+V8Prx/lLVV199ag+ODm9SO/uqpe\nVVVvbK5//aExfm6M8Tu1/j8pv1xVvzHn89J56Aer6qvGGNeMMS6o9Unr5/mGGeNm7ri8bvPvrWOM\n+8c6Jey7NjeHpPPVvWOMB8YYPzSOT9catf4/m597oTcWv7rWPxTfd8TfjjVjrM85v0uLgBuwP7z1\n+g1jjKer6lNV9V9W1V+bud4ba31D+LuP+PNban29XFXTj+LfrfW1uXTe26RcPVPrKPK31Tr65qj3\nfWlVvbKqPoqXrxjrxyHcs0klu/AsNuFHxzqV+hfHGG/D647NE+KPgj2zCU390ar64dVq9Ztbf/uS\nqvrLlVO/tl1UVZ/Yeu2Zqjr9zIF/VlXfMtbPVLikqv7S5vUL5mzzarX6I5t1vquqfnG1Wn12zuel\n89BHap3i+WCtx9yba+tC85hxs+u4PP1/e76+1iH0X1tV/36t08Ok883Hq+rLa50u/Y5aj6kfxd//\nWVX9pTHGa8cYt9b6R98ZY6l5DntPVf3karV6du4GdtY/xvg3N9/xl+euXzrP/Wu1Tvv4Sb64SRl7\nXVWdqvVjFH7ziM8e529X1XeEMf1C18/SeW21Wv3YJh3stlpnjzy6/Z6xfl7lj1TVd61Wq9Pj5Ter\n6kur6uqq+jdqfV7+G9uffQHfVJ97DMovVdUvjDFet/mbY/OEeBNoj2z+T/2PVNWnq+pbt/52a60j\nCb5ttVr9q+Yqn62q7QfKXlLr5wpVrXNEf7zW/2fyzloPtKqqB8YYX40Hct252YY78dpXc6Wr1er3\nVqvVz1fV148x/u3m9knnq/+hql5d62dxXVhVP1VbkUBVcdzsOi4/tXn/X1utVk9vwuf/fq1/gErn\nldVq9exqtfqN1Wr1+5sUkW+t9Xg6fUH4Z6vqd2p9Y/anaz22HjhiPfEctonm++OFSISTPEeOMb6i\nqn6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po2mbee5KqTG8ZkjXw9vpOekcn/oVz0cpxT2ldnfSG9N1bKevdCompmv1VBGMbZrG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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe606f1b3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20.0, 20.0)\n",
    "f, ax = plt.subplots(nrows=3, ncols=5)\n",
    "\n",
    "im_samples = []\n",
    "\n",
    "for row in range(3):\n",
    "    for i, j in enumerate(np.sort(np.random.randint(0, c5_target.shape[0], size=5))):\n",
    "        im = c5_data[j].reshape((64, 64, 1))\n",
    "        house_num = ''\n",
    "        for k in np.arange(c5_target[j,0]):\n",
    "            house_num += str(c5_target[j,k+1])\n",
    "        house_num += \"--\" + str(j)\n",
    "        im_samples.extend([j])\n",
    "        ax[row, i].axis('off')\n",
    "        ax[row, i].set_title(house_num, loc='center')\n",
    "        ax[row, i].imshow(im[:,:,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 1 1 3 2 5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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9e4iMDK332RHjzNMMGft6xjHNyJjP580Z5+fnzRkZ14ZR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe606f10d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(c5_data[50].reshape(64, 64), cmap='gray')\n",
    "print(c5_target[50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = (44, 46, 47, 46, 37, 48, 40, 42, 49, 38, 39, 41, 43, 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "c5_data = np.delete(c5_data, a, axis=0)\n",
    "print(c5_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 6)\n"
     ]
    }
   ],
   "source": [
    "c5_target = np.delete(c5_target, a, axis=0)\n",
    "print(c5_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 20, 3, padding=(1, 1))\n",
    "        self.conv2 = nn.Conv2d(20, 40, 3, padding=(1, 1))\n",
    "        self.conv3 = nn.Conv2d(40, 80, 3, padding=(1, 1))\n",
    "        self.conv4 = nn.Conv2d(80, 120, 3, padding=(1, 1))\n",
    "        self.conv5 = nn.Conv2d(120, 160, 3, padding=(1, 1))\n",
    "        self.conv6 = nn.Conv2d(160, 200, 3, padding=(1, 1))\n",
    "        self.conv7 = nn.Conv2d(200, 240, 3, padding=(1, 1))\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.drop2d = nn.Dropout2d(0.25)\n",
    "        self.drop1 = nn.Dropout(0.35)\n",
    "        self.drop2 = nn.Dropout(0.5)\n",
    "        self.bnorm1 = nn.BatchNorm2d(80)\n",
    "        self.bnorm2 = nn.BatchNorm2d(120)\n",
    "        self.bnorm3 = nn.BatchNorm2d(160)\n",
    "        self.bnorm4 = nn.BatchNorm2d(200)\n",
    "        self.bnorm5 = nn.BatchNorm2d(240)\n",
    "        self.FC = nn.Linear(960, 1080)\n",
    "        self.digitlength = nn.Linear(1080, 7)\n",
    "        self.digit1 = nn.Linear(1080, 10)\n",
    "        self.digit2 = nn.Linear(1080, 10)\n",
    "        self.digit3 = nn.Linear(1080, 10)\n",
    "        self.digit4 = nn.Linear(1080, 10)\n",
    "        self.digit5 = nn.Linear(1080, 10)\n",
    "        \n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                init.kaiming_normal(m.weight)\n",
    "                m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                init.kaiming_normal(m.weight)\n",
    "                m.bias.data.zero_()\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.drop2d(x)\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = self.drop2d(x)\n",
    "        x = self.bnorm1(F.relu(self.conv3(x)))\n",
    "        x = self.pool(self.bnorm2(F.relu(self.conv4(x))))\n",
    "        x = self.drop1(x)\n",
    "        x = self.pool(self.bnorm3(F.relu(self.conv5(x))))\n",
    "        x = self.drop1(x)\n",
    "        x = self.pool(self.bnorm4(F.relu(self.conv6(x))))\n",
    "        x = self.drop1(x)\n",
    "        x = self.pool(self.bnorm5(F.relu(self.conv7(x))))\n",
    "        x = x.view(-1, 960)\n",
    "        x = self.drop1(x)\n",
    "        x = F.relu(self.FC(x))\n",
    "        x = self.drop2(x)\n",
    "        yl = self.digitlength(x)\n",
    "        y1 = self.digit1(x)\n",
    "        y2 = self.digit2(x)\n",
    "        y3 = self.digit3(x)\n",
    "        y4 = self.digit4(x)\n",
    "        y5 = self.digit5(x)\n",
    "        return [yl, y1, y2, y3, y4, y5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net (\n",
       "  (conv1): Conv2d(1, 20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2): Conv2d(20, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3): Conv2d(40, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4): Conv2d(80, 120, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5): Conv2d(120, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv6): Conv2d(160, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv7): Conv2d(200, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (pool): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
       "  (drop2d): Dropout2d (p=0.25)\n",
       "  (drop1): Dropout (p = 0.35)\n",
       "  (drop2): Dropout (p = 0.5)\n",
       "  (bnorm1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm2): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm4): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm5): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (FC): Linear (960 -> 1080)\n",
       "  (digitlength): Linear (1080 -> 7)\n",
       "  (digit1): Linear (1080 -> 10)\n",
       "  (digit2): Linear (1080 -> 10)\n",
       "  (digit3): Linear (1080 -> 10)\n",
       "  (digit4): Linear (1080 -> 10)\n",
       "  (digit5): Linear (1080 -> 10)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for param in net.parameters():\n",
    "    if(param.grad is not None):\n",
    "        print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.FloatTensor torch.Size([39, 1, 64, 64])\n",
      "torch.LongTensor torch.Size([39, 6])\n"
     ]
    }
   ],
   "source": [
    "c5_data_tensor = torch.from_numpy(c5_data)\n",
    "c5_target_tensor = torch.from_numpy(c5_target).type(torch.LongTensor)\n",
    "print(c5_data_tensor.type(), c5_data_tensor.size())\n",
    "print(c5_target_tensor.type(), c5_target_tensor.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "objective = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(net.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 200\n",
    "batch_size = 5\n",
    "num_train =  c5_data.shape[0]\n",
    "iter_per_epoch = num_train // batch_size\n",
    "print_every = 3\n",
    "print(iter_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epoch_losses = {i:[] for i in range(num_epochs)}\n",
    "loss_history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09526059031486511\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.007006215862929821 loss2 :  0.024568915367126465 loss3 :  0.04151954501867294\n",
      "loss4 :  0.0038614273071289062 loss5 :  0.01830429956316948\n",
      "Iteration :  4  /  7\n",
      "loss :  0.053737640380859375\n",
      "lossl :  6.771087555534905e-06 loss1 :  0.001122808433137834 loss2 :  0.011043453589081764 loss3 :  0.02669849433004856\n",
      "loss4 :  0.00892419833689928 loss5 :  0.005941915325820446\n",
      "Iteration :  7  /  7\n",
      "loss :  0.24038276076316833\n",
      "lossl :  5.245208740234375e-06 loss1 :  0.0028625489212572575 loss2 :  0.07855310291051865 loss3 :  0.11320038139820099\n",
      "loss4 :  0.006161498837172985 loss5 :  0.03959999233484268\n",
      "time taken :  0.5777287483215332\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  2.8527047634124756\n",
      "lossl :  0.0 loss1 :  0.0032701969612389803 loss2 :  1.6612482070922852 loss3 :  0.47418197989463806\n",
      "loss4 :  0.6464740633964539 loss5 :  0.06753037869930267\n",
      "Iteration :  4  /  7\n",
      "loss :  3.004786491394043\n",
      "lossl :  0.0 loss1 :  0.5463919639587402 loss2 :  0.05032854154706001 loss3 :  0.1809401959180832\n",
      "loss4 :  0.9308603405952454 loss5 :  1.2962652444839478\n",
      "Iteration :  7  /  7\n",
      "loss :  0.7612659931182861\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.28623470664024353 loss2 :  0.037790536880493164 loss3 :  0.2127586305141449\n",
      "loss4 :  0.15822887420654297 loss5 :  0.06625280529260635\n",
      "time taken :  0.16524934768676758\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 1/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  2.042464256286621\n",
      "lossl :  0.0 loss1 :  0.7840444445610046 loss2 :  0.04276289790868759 loss3 :  0.7553142309188843\n",
      "loss4 :  0.4485747814178467 loss5 :  0.011767959222197533\n",
      "Iteration :  4  /  7\n",
      "loss :  0.4605637788772583\n",
      "lossl :  7.605552673339844e-05 loss1 :  0.007298135664314032 loss2 :  0.07757506519556046 loss3 :  0.03762035444378853\n",
      "loss4 :  0.2045884132385254 loss5 :  0.13340575993061066\n",
      "Iteration :  7  /  7\n",
      "loss :  0.044162895530462265\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0036008358001708984 loss2 :  0.00129022600594908 loss3 :  0.016714954748749733\n",
      "loss4 :  0.019878674298524857 loss5 :  0.002678012941032648\n",
      "time taken :  0.5666506290435791\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.4507288932800293\n",
      "lossl :  0.0 loss1 :  0.0019068240653723478 loss2 :  0.3808281421661377 loss3 :  0.5937207937240601\n",
      "loss4 :  0.20458412170410156 loss5 :  0.26968914270401\n",
      "Iteration :  4  /  7\n",
      "loss :  1.3629581928253174\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.014622879214584827 loss2 :  0.8482583165168762 loss3 :  0.10872285068035126\n",
      "loss4 :  0.3344544768333435 loss5 :  0.05689864233136177\n",
      "Iteration :  7  /  7\n",
      "loss :  2.661951780319214\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.4409356713294983 loss2 :  0.0945964828133583 loss3 :  0.131144717335701\n",
      "loss4 :  0.8480660319328308 loss5 :  1.1472084522247314\n",
      "time taken :  0.1650071144104004\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 2/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.34462490677833557\n",
      "lossl :  2.19345088225964e-06 loss1 :  0.005066871643066406 loss2 :  0.017851877957582474 loss3 :  0.2255755364894867\n",
      "loss4 :  0.0430484302341938 loss5 :  0.053079985082149506\n",
      "Iteration :  4  /  7\n",
      "loss :  0.017520712688565254\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00025472641573287547 loss2 :  0.009199905209243298 loss3 :  0.0012350082397460938\n",
      "loss4 :  0.003608322236686945 loss5 :  0.003222560975700617\n",
      "Iteration :  7  /  7\n",
      "loss :  0.032506704330444336\n",
      "lossl :  0.0 loss1 :  0.005870342254638672 loss2 :  0.005775642581284046 loss3 :  0.0065503595396876335\n",
      "loss4 :  0.009763861075043678 loss5 :  0.004546499345451593\n",
      "time taken :  0.5954561233520508\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  2.026609420776367\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.10349626839160919 loss2 :  0.8240594863891602 loss3 :  0.06046142429113388\n",
      "loss4 :  0.4062820374965668 loss5 :  0.6323097348213196\n",
      "Iteration :  4  /  7\n",
      "loss :  1.779663324356079\n",
      "lossl :  0.0 loss1 :  0.00022592543973587453 loss2 :  0.8438242673873901 loss3 :  0.32922595739364624\n",
      "loss4 :  0.5675938725471497 loss5 :  0.038793373852968216\n",
      "Iteration :  7  /  7\n",
      "loss :  0.32259616255760193\n",
      "lossl :  0.0 loss1 :  0.004718446638435125 loss2 :  0.23548546433448792 loss3 :  0.044208623468875885\n",
      "loss4 :  0.013372659683227539 loss5 :  0.02481095865368843\n",
      "time taken :  0.17023372650146484\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 3/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.15972957015037537\n",
      "lossl :  2.47955313170678e-06 loss1 :  0.020172977820038795 loss2 :  0.037297677248716354 loss3 :  0.06870055198669434\n",
      "loss4 :  0.024124670773744583 loss5 :  0.009431218728423119\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07941264659166336\n",
      "lossl :  0.0 loss1 :  0.005272007081657648 loss2 :  0.005885505583137274 loss3 :  0.027097653597593307\n",
      "loss4 :  0.04031548649072647 loss5 :  0.0008419990772381425\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03167872503399849\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.013776826672255993 loss2 :  0.0018031119834631681 loss3 :  0.008546734228730202\n",
      "loss4 :  0.0029615401290357113 loss5 :  0.004588508512824774\n",
      "time taken :  0.6020970344543457\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.9530563950538635\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0004790305974893272 loss2 :  0.5396320223808289 loss3 :  0.14705276489257812\n",
      "loss4 :  0.2655467987060547 loss5 :  0.0003456115664448589\n",
      "Iteration :  4  /  7\n",
      "loss :  1.8932349681854248\n",
      "lossl :  0.0 loss1 :  0.1091645210981369 loss2 :  0.4182646870613098 loss3 :  0.045928023755550385\n",
      "loss4 :  0.4338921904563904 loss5 :  0.8859856724739075\n",
      "Iteration :  7  /  7\n",
      "loss :  0.41114747524261475\n",
      "lossl :  0.0 loss1 :  0.0023713111877441406 loss2 :  0.04313797876238823 loss3 :  0.24809464812278748\n",
      "loss4 :  0.05870962142944336 loss5 :  0.05883393436670303\n",
      "time taken :  0.17332744598388672\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 4/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.4398309290409088\n",
      "lossl :  3.24249276673072e-06 loss1 :  0.09335837513208389 loss2 :  0.07595272362232208 loss3 :  0.044255733489990234\n",
      "loss4 :  0.17709216475486755 loss5 :  0.04916868358850479\n",
      "Iteration :  4  /  7\n",
      "loss :  0.20328311622142792\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.01947169378399849 loss2 :  0.010054970160126686 loss3 :  0.08867807686328888\n",
      "loss4 :  0.060556747019290924 loss5 :  0.024519633501768112\n",
      "Iteration :  7  /  7\n",
      "loss :  0.06710565090179443\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.018174339085817337 loss2 :  0.03061370924115181 loss3 :  0.007061767391860485\n",
      "loss4 :  0.007935523986816406 loss5 :  0.0033189773093909025\n",
      "time taken :  0.618579626083374\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.056729793548583984\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.0017894267803058028 loss2 :  0.01610584184527397 loss3 :  0.0059701441787183285\n",
      "loss4 :  0.007397937588393688 loss5 :  0.025465775281190872\n",
      "Iteration :  4  /  7\n",
      "loss :  0.27362942695617676\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.003495120909065008 loss2 :  0.03528022766113281 loss3 :  0.1826692372560501\n",
      "loss4 :  0.015925073996186256 loss5 :  0.03625965118408203\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0421239398419857\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0010700703132897615 loss2 :  0.020067214965820312 loss3 :  0.0044980524107813835\n",
      "loss4 :  0.0091085908934474 loss5 :  0.007379055023193359\n",
      "time taken :  0.1816120147705078\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 5/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.2785864472389221\n",
      "lossl :  8.239746239269152e-05 loss1 :  0.03712492063641548 loss2 :  0.05167841911315918 loss3 :  0.1662839949131012\n",
      "loss4 :  0.012023639865219593 loss5 :  0.011393070220947266\n",
      "Iteration :  4  /  7\n",
      "loss :  0.025372648611664772\n",
      "lossl :  0.0 loss1 :  0.00019006729417014867 loss2 :  0.004047823138535023 loss3 :  0.0012458801502361894\n",
      "loss4 :  0.01550588570535183 loss5 :  0.004382991697639227\n",
      "Iteration :  7  /  7\n",
      "loss :  0.09936802834272385\n",
      "lossl :  0.0 loss1 :  0.0004119873046875 loss2 :  0.02149968221783638 loss3 :  0.06947801262140274\n",
      "loss4 :  0.0046714781783521175 loss5 :  0.003306865692138672\n",
      "time taken :  0.5628292560577393\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06477232277393341\n",
      "lossl :  0.0 loss1 :  0.0013821125030517578 loss2 :  0.028089141473174095 loss3 :  0.005284452345222235\n",
      "loss4 :  0.00955190695822239 loss5 :  0.020464707165956497\n",
      "Iteration :  4  /  7\n",
      "loss :  0.15139630436897278\n",
      "lossl :  0.0 loss1 :  0.0010320186847820878 loss2 :  0.08225972950458527 loss3 :  0.009149789810180664\n",
      "loss4 :  0.020429443567991257 loss5 :  0.03852532058954239\n",
      "Iteration :  7  /  7\n",
      "loss :  0.09491043537855148\n",
      "lossl :  0.0 loss1 :  0.0027163983322679996 loss2 :  0.04529838636517525 loss3 :  0.0034420012962073088\n",
      "loss4 :  0.02467374876141548 loss5 :  0.018779898062348366\n",
      "time taken :  0.16599488258361816\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 6/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0614224411547184\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.004928684327751398 loss2 :  0.006989955902099609 loss3 :  0.02612900733947754\n",
      "loss4 :  0.020810937508940697 loss5 :  0.0025637627113610506\n",
      "Iteration :  4  /  7\n",
      "loss :  0.09501814842224121\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.00560994166880846 loss2 :  0.004317665006965399 loss3 :  0.07352447509765625\n",
      "loss4 :  0.010695934295654297 loss5 :  0.0008687973022460938\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4435581564903259\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.11429576575756073 loss2 :  0.006443691439926624 loss3 :  0.09390030056238174\n",
      "loss4 :  0.02448282204568386 loss5 :  0.20443502068519592\n",
      "time taken :  0.5590357780456543\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.47500157356262207\n",
      "lossl :  0.0 loss1 :  0.015406799502670765 loss2 :  0.02670736238360405 loss3 :  0.020161723718047142\n",
      "loss4 :  0.10526265949010849 loss5 :  0.30746302008628845\n",
      "Iteration :  4  /  7\n",
      "loss :  0.3660393953323364\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0007585525745525956 loss2 :  0.34925732016563416 loss3 :  0.0029956817161291838\n",
      "loss4 :  0.00948486290872097 loss5 :  0.0035428046248853207\n",
      "Iteration :  7  /  7\n",
      "loss :  0.2043551355600357\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0020983696449548006 loss2 :  0.12852105498313904 loss3 :  0.019019698724150658\n",
      "loss4 :  0.019302796572446823 loss5 :  0.03541283681988716\n",
      "time taken :  0.1853179931640625\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 7/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.30304133892059326\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.029293537139892578 loss2 :  0.009896611794829369 loss3 :  0.1338047981262207\n",
      "loss4 :  0.03273153305053711 loss5 :  0.09731440246105194\n",
      "Iteration :  4  /  7\n",
      "loss :  0.11643056571483612\n",
      "lossl :  0.0 loss1 :  0.001671266509220004 loss2 :  0.020960474386811256 loss3 :  0.014248276129364967\n",
      "loss4 :  0.04237713664770126 loss5 :  0.03717341274023056\n",
      "Iteration :  7  /  7\n",
      "loss :  0.024323701858520508\n",
      "lossl :  0.0 loss1 :  0.001713657402433455 loss2 :  0.0110922334715724 loss3 :  0.005810737609863281\n",
      "loss4 :  0.0017599106067791581 loss5 :  0.003947162535041571\n",
      "time taken :  0.6288628578186035\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05987162888050079\n",
      "lossl :  0.0 loss1 :  0.002229404402896762 loss2 :  0.03450632095336914 loss3 :  0.0005570411449298263\n",
      "loss4 :  0.010079288855195045 loss5 :  0.012499570846557617\n",
      "Iteration :  4  /  7\n",
      "loss :  0.21292142570018768\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.002468013670295477 loss2 :  0.14230260252952576 loss3 :  0.021593760699033737\n",
      "loss4 :  0.01917290687561035 loss5 :  0.02738366089761257\n",
      "Iteration :  7  /  7\n",
      "loss :  0.23467805981636047\n",
      "lossl :  0.0 loss1 :  0.0009919166332110763 loss2 :  0.1327749341726303 loss3 :  0.02225198782980442\n",
      "loss4 :  0.04399266093969345 loss5 :  0.03466656059026718\n",
      "time taken :  0.16411495208740234\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 8/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.6755239367485046\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0012001037830486894 loss2 :  0.5454387664794922 loss3 :  0.012783050537109375\n",
      "loss4 :  0.04154014587402344 loss5 :  0.07456178963184357\n",
      "Iteration :  4  /  7\n",
      "loss :  0.030310295522212982\n",
      "lossl :  0.0 loss1 :  0.0014693737030029297 loss2 :  0.003986644558608532 loss3 :  0.015415859408676624\n",
      "loss4 :  0.002655601594597101 loss5 :  0.006782817654311657\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1675117015838623\n",
      "lossl :  0.0 loss1 :  0.0011240958701819181 loss2 :  0.046529483050107956 loss3 :  0.09878110885620117\n",
      "loss4 :  0.008364963345229626 loss5 :  0.012712049297988415\n",
      "time taken :  0.5573067665100098\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.037955090403556824\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.00012216568575240672 loss2 :  0.02507324144244194 loss3 :  0.003212547395378351\n",
      "loss4 :  0.002869844436645508 loss5 :  0.006676912307739258\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2695329785346985\n",
      "lossl :  0.0 loss1 :  0.03215456008911133 loss2 :  0.12714603543281555 loss3 :  0.01566319540143013\n",
      "loss4 :  0.02636241912841797 loss5 :  0.068206787109375\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0990811362862587\n",
      "lossl :  0.0 loss1 :  0.002765130950137973 loss2 :  0.009755467996001244 loss3 :  0.07205267250537872\n",
      "loss4 :  0.006658077239990234 loss5 :  0.007849788293242455\n",
      "time taken :  0.1651012897491455\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 9/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06675539165735245\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.001946306205354631 loss2 :  0.03844790533185005 loss3 :  0.00795588456094265\n",
      "loss4 :  0.010066556744277477 loss5 :  0.00833826046437025\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010049152188003063\n",
      "lossl :  0.0 loss1 :  0.0002522468566894531 loss2 :  0.0009685516124591231 loss3 :  0.0005169868236407638\n",
      "loss4 :  0.002082443330436945 loss5 :  0.006228923797607422\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5061944127082825\n",
      "lossl :  2.098083541568485e-06 loss1 :  0.0029867650009691715 loss2 :  0.07807488739490509 loss3 :  0.2660048007965088\n",
      "loss4 :  0.0534358024597168 loss5 :  0.10569004714488983\n",
      "time taken :  0.5673203468322754\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.8055218458175659\n",
      "lossl :  0.0 loss1 :  0.0029120445251464844 loss2 :  0.4003036618232727 loss3 :  0.34690800309181213\n",
      "loss4 :  0.04880552366375923 loss5 :  0.00659255962818861\n",
      "Iteration :  4  /  7\n",
      "loss :  0.025466490536928177\n",
      "lossl :  0.0 loss1 :  0.0035241127479821444 loss2 :  0.010154676623642445 loss3 :  0.004844474606215954\n",
      "loss4 :  0.0026700973976403475 loss5 :  0.004273128695785999\n",
      "Iteration :  7  /  7\n",
      "loss :  1.2803174257278442\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lossl :  4.76837158203125e-07 loss1 :  0.1136503666639328 loss2 :  0.06520514190196991 loss3 :  0.19937406480312347\n",
      "loss4 :  0.2679515480995178 loss5 :  0.6341358423233032\n",
      "time taken :  0.19089484214782715\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 10/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.23324967920780182\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.03816509246826172 loss2 :  0.13515286147594452 loss3 :  0.003724002745002508\n",
      "loss4 :  0.016074324026703835 loss5 :  0.04013318940997124\n",
      "Iteration :  4  /  7\n",
      "loss :  0.09367118030786514\n",
      "lossl :  0.0 loss1 :  0.0011501312255859375 loss2 :  0.02588520012795925 loss3 :  0.004542016889899969\n",
      "loss4 :  0.0013545036781579256 loss5 :  0.06073932722210884\n",
      "Iteration :  7  /  7\n",
      "loss :  0.012548064813017845\n",
      "lossl :  0.0 loss1 :  8.00132766016759e-05 loss2 :  0.00021829604520462453 loss3 :  0.0008697509765625\n",
      "loss4 :  0.0002689361572265625 loss5 :  0.011111068539321423\n",
      "time taken :  0.5740506649017334\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.21856431663036346\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0028944015502929688 loss2 :  0.001275730086490512 loss3 :  0.1970890462398529\n",
      "loss4 :  0.014423990622162819 loss5 :  0.002880573272705078\n",
      "Iteration :  4  /  7\n",
      "loss :  1.1141067743301392\n",
      "lossl :  0.0 loss1 :  0.10990557819604874 loss2 :  0.027104664593935013 loss3 :  0.13603749871253967\n",
      "loss4 :  0.252436101436615 loss5 :  0.5886229276657104\n",
      "Iteration :  7  /  7\n",
      "loss :  0.297810822725296\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.04142904281616211 loss2 :  0.14109887182712555 loss3 :  0.01257634162902832\n",
      "loss4 :  0.011184406466782093 loss5 :  0.09152188152074814\n",
      "time taken :  0.17595577239990234\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 11/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01596860960125923\n",
      "lossl :  0.0 loss1 :  0.0011600017314776778 loss2 :  0.006452226545661688 loss3 :  0.0026576996315270662\n",
      "loss4 :  0.005044460296630859 loss5 :  0.0006542205810546875\n",
      "Iteration :  4  /  7\n",
      "loss :  1.2686856985092163\n",
      "lossl :  0.0 loss1 :  0.0031333446968346834 loss2 :  0.0762932300567627 loss3 :  0.1358795166015625\n",
      "loss4 :  0.9213758707046509 loss5 :  0.13200373947620392\n",
      "Iteration :  7  /  7\n",
      "loss :  0.050765130668878555\n",
      "lossl :  0.0 loss1 :  0.0004958153003826737 loss2 :  0.0185223575681448 loss3 :  0.011031818576157093\n",
      "loss4 :  0.013678264804184437 loss5 :  0.007036876864731312\n",
      "time taken :  0.5713675022125244\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1922639012336731\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0018324851989746094 loss2 :  0.034863851964473724 loss3 :  0.0035445690155029297\n",
      "loss4 :  0.028745174407958984 loss5 :  0.12327752262353897\n",
      "Iteration :  4  /  7\n",
      "loss :  0.9977390766143799\n",
      "lossl :  0.0 loss1 :  0.0006734848138876259 loss2 :  0.4721972942352295 loss3 :  0.351787805557251\n",
      "loss4 :  0.08039417117834091 loss5 :  0.09268634021282196\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07421717047691345\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0015988349914550781 loss2 :  0.040360260754823685 loss3 :  0.0073434351943433285\n",
      "loss4 :  0.007382774259895086 loss5 :  0.017531681805849075\n",
      "time taken :  0.17763042449951172\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 12/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.014226436614990234\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.002189063932746649 loss2 :  0.004197311587631702 loss3 :  0.0009106636280193925\n",
      "loss4 :  0.0008958816761150956 loss5 :  0.0060333251021802425\n",
      "Iteration :  4  /  7\n",
      "loss :  0.018309354782104492\n",
      "lossl :  0.0 loss1 :  0.00040378569974564016 loss2 :  0.004103613086044788 loss3 :  0.003943157382309437\n",
      "loss4 :  0.0065784454345703125 loss5 :  0.0032803534995764494\n",
      "Iteration :  7  /  7\n",
      "loss :  0.423409640789032\n",
      "lossl :  0.0 loss1 :  0.028390979394316673 loss2 :  0.02935175970196724 loss3 :  0.2359497845172882\n",
      "loss4 :  0.12180829048156738 loss5 :  0.007908821105957031\n",
      "time taken :  0.5886154174804688\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.046830035746097565\n",
      "lossl :  0.0 loss1 :  0.00322723388671875 loss2 :  0.01635007932782173 loss3 :  0.015518712811172009\n",
      "loss4 :  0.010066032409667969 loss5 :  0.0016679763793945312\n",
      "Iteration :  4  /  7\n",
      "loss :  3.022465705871582\n",
      "lossl :  0.0 loss1 :  0.5222989320755005 loss2 :  0.5312054753303528 loss3 :  0.16072487831115723\n",
      "loss4 :  0.4113236367702484 loss5 :  1.3969125747680664\n",
      "Iteration :  7  /  7\n",
      "loss :  0.7584421634674072\n",
      "lossl :  0.0 loss1 :  0.003771114395931363 loss2 :  0.12436380237340927 loss3 :  0.24783582985401154\n",
      "loss4 :  0.26893097162246704 loss5 :  0.11354038864374161\n",
      "time taken :  0.17586994171142578\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 13/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2468733787536621\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0053008077666163445 loss2 :  0.004519748501479626 loss3 :  0.17184552550315857\n",
      "loss4 :  0.03688950464129448 loss5 :  0.028317689895629883\n",
      "Iteration :  4  /  7\n",
      "loss :  0.019846059381961823\n",
      "lossl :  4.472732689464465e-05 loss1 :  0.009000206366181374 loss2 :  0.002863979432731867 loss3 :  0.0021075247786939144\n",
      "loss4 :  0.0005327224498614669 loss5 :  0.005296898074448109\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1337941735982895\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.00043487548828125 loss2 :  0.08298011124134064 loss3 :  0.014545058831572533\n",
      "loss4 :  0.016365623101592064 loss5 :  0.019467640668153763\n",
      "time taken :  0.6031439304351807\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05540008842945099\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0006872176891192794 loss2 :  0.011568593792617321 loss3 :  0.007302045822143555\n",
      "loss4 :  0.02364063262939453 loss5 :  0.012201404199004173\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07766351848840714\n",
      "lossl :  0.0 loss1 :  0.0002719879266805947 loss2 :  0.01013937033712864 loss3 :  0.0007835387950763106\n",
      "loss4 :  0.0065634725615382195 loss5 :  0.059905149042606354\n",
      "Iteration :  7  /  7\n",
      "loss :  0.021409131586551666\n",
      "lossl :  0.0 loss1 :  8.039474778342992e-05 loss2 :  0.001615333603695035 loss3 :  0.006214332766830921\n",
      "loss4 :  0.0034016608260571957 loss5 :  0.010097408667206764\n",
      "time taken :  0.18503189086914062\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 14/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.020615721121430397\n",
      "lossl :  0.0 loss1 :  0.0004788398800883442 loss2 :  0.014299964532256126 loss3 :  0.0029458999633789062\n",
      "loss4 :  0.0003092765691690147 loss5 :  0.0025817393325269222\n",
      "Iteration :  4  /  7\n",
      "loss :  0.307082861661911\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0012553215492516756 loss2 :  0.18262676894664764 loss3 :  0.05388283729553223\n",
      "loss4 :  0.013629769906401634 loss5 :  0.055687569081783295\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4171473979949951\n",
      "lossl :  0.0 loss1 :  0.0008866310236044228 loss2 :  0.00963296927511692 loss3 :  0.2606813907623291\n",
      "loss4 :  0.003704071044921875 loss5 :  0.14224234223365784\n",
      "time taken :  0.5835225582122803\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  2.8680992126464844\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.27517977356910706 loss2 :  0.4959980547428131 loss3 :  0.3075181543827057\n",
      "loss4 :  1.171208381652832 loss5 :  0.6181939840316772\n",
      "Iteration :  4  /  7\n",
      "loss :  0.6678943634033203\n",
      "lossl :  0.0 loss1 :  4.081726001459174e-05 loss2 :  0.10473938286304474 loss3 :  0.13551560044288635\n",
      "loss4 :  0.3043515086174011 loss5 :  0.12324704974889755\n",
      "Iteration :  7  /  7\n",
      "loss :  1.916605830192566\n",
      "lossl :  0.0 loss1 :  0.40437382459640503 loss2 :  0.6022621989250183 loss3 :  0.00783681869506836\n",
      "loss4 :  0.08741579204797745 loss5 :  0.814717173576355\n",
      "time taken :  0.1644294261932373\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 15/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.3138340413570404\n",
      "lossl :  0.0 loss1 :  0.005885315127670765 loss2 :  0.009159088134765625 loss3 :  0.04546480253338814\n",
      "loss4 :  0.2212606966495514 loss5 :  0.032064151018857956\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03135061264038086\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0006479740259237587 loss2 :  0.021342705935239792 loss3 :  0.0005863189580850303\n",
      "loss4 :  0.0018644332885742188 loss5 :  0.00690879812464118\n",
      "Iteration :  7  /  7\n",
      "loss :  0.25793635845184326\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.002698564436286688 loss2 :  0.11238918453454971 loss3 :  0.02392401732504368\n",
      "loss4 :  0.040844012051820755 loss5 :  0.07808039337396622\n",
      "time taken :  0.5683996677398682\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.08751005679368973\n",
      "lossl :  0.0 loss1 :  0.00294075021520257 loss2 :  0.04741325229406357 loss3 :  0.003899478819221258\n",
      "loss4 :  0.008358287625014782 loss5 :  0.024898290634155273\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0066811563447117805\n",
      "lossl :  0.0 loss1 :  0.0011152267688885331 loss2 :  0.00057220458984375 loss3 :  0.0017587661277502775\n",
      "loss4 :  0.002026176545768976 loss5 :  0.0012087821960449219\n",
      "Iteration :  7  /  7\n",
      "loss :  1.3370776176452637\n",
      "lossl :  0.0 loss1 :  0.35425546765327454 loss2 :  0.39272230863571167 loss3 :  0.0029932023026049137\n",
      "loss4 :  0.02937793731689453 loss5 :  0.5577285885810852\n",
      "time taken :  0.18963623046875\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 16/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.041159868240356445\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.004034710116684437 loss2 :  0.003803253173828125 loss3 :  0.022136926651000977\n",
      "loss4 :  0.008514595218002796 loss5 :  0.002669906709343195\n",
      "Iteration :  4  /  7\n",
      "loss :  0.3362579047679901\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.016576766967773438 loss2 :  0.18820981681346893 loss3 :  0.06812477111816406\n",
      "loss4 :  0.023198317736387253 loss5 :  0.04014625400304794\n",
      "Iteration :  7  /  7\n",
      "loss :  0.028168201446533203\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.005018711090087891 loss2 :  0.0023813247680664062 loss3 :  0.003701114561408758\n",
      "loss4 :  0.016114044934511185 loss5 :  0.00095281598623842\n",
      "time taken :  0.5924453735351562\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.08009390532970428\n",
      "lossl :  0.0 loss1 :  0.002971696900203824 loss2 :  0.018824005499482155 loss3 :  0.0061945440247654915\n",
      "loss4 :  0.012318229302763939 loss5 :  0.03978543356060982\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07385306060314178\n",
      "lossl :  0.0 loss1 :  0.00021848679170943797 loss2 :  0.024843979626893997 loss3 :  0.009386086836457253\n",
      "loss4 :  0.03281891345977783 loss5 :  0.006585597991943359\n",
      "Iteration :  7  /  7\n",
      "loss :  0.32731688022613525\n",
      "lossl :  0.0 loss1 :  0.03165025636553764 loss2 :  0.0021826743613928556 loss3 :  0.03928690031170845\n",
      "loss4 :  0.05673856660723686 loss5 :  0.19745846092700958\n",
      "time taken :  0.16559886932373047\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 17/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.006121015176177025\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.003181409789249301 loss2 :  0.0001924514799611643 loss3 :  0.0001010894775390625\n",
      "loss4 :  0.002368927001953125 loss5 :  0.0002768516424112022\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2512335777282715\n",
      "lossl :  0.0 loss1 :  0.002952003385871649 loss2 :  0.015064430423080921 loss3 :  0.06397251784801483\n",
      "loss4 :  0.03383612632751465 loss5 :  0.13540849089622498\n",
      "Iteration :  7  /  7\n",
      "loss :  0.13050007820129395\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0006855010869912803 loss2 :  0.0020629882346838713 loss3 :  0.007597160525619984\n",
      "loss4 :  0.0005047798040322959 loss5 :  0.11964926868677139\n",
      "time taken :  0.5552225112915039\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.6374717354774475\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.24025921523571014 loss2 :  0.15417955815792084 loss3 :  0.003506279084831476\n",
      "loss4 :  0.04373221471905708 loss5 :  0.1957942545413971\n",
      "Iteration :  4  /  7\n",
      "loss :  0.017734240740537643\n",
      "lossl :  0.0 loss1 :  0.0013208389282226562 loss2 :  0.00858526211231947 loss3 :  0.001924705458804965\n",
      "loss4 :  0.0010421753395348787 loss5 :  0.004861259367316961\n",
      "Iteration :  7  /  7\n",
      "loss :  0.042563676834106445\n",
      "lossl :  0.0 loss1 :  0.0007101058727130294 loss2 :  0.019594479352235794 loss3 :  0.008423661813139915\n",
      "loss4 :  0.0024039268027991056 loss5 :  0.011431503109633923\n",
      "time taken :  0.16637134552001953\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 18/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.17703454196453094\n",
      "lossl :  0.0 loss1 :  0.12727239727973938 loss2 :  0.0059340475127100945 loss3 :  0.03649730607867241\n",
      "loss4 :  0.0059912679716944695 loss5 :  0.0013395309215411544\n",
      "Iteration :  4  /  7\n",
      "loss :  0.041086483746767044\n",
      "lossl :  2.3365020751953125e-05 loss1 :  0.018346786499023438 loss2 :  0.006740665528923273 loss3 :  0.0025463104248046875\n",
      "loss4 :  0.006793022155761719 loss5 :  0.006636333651840687\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01296162698417902\n",
      "lossl :  0.0 loss1 :  0.008671427145600319 loss2 :  0.0008365631219930947 loss3 :  0.00042600632878020406\n",
      "loss4 :  0.0023020743392407894 loss5 :  0.0007255554082803428\n",
      "time taken :  0.5586836338043213\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.14299622178077698\n",
      "lossl :  0.0 loss1 :  0.00836257915943861 loss2 :  0.0025913238059729338 loss3 :  0.012188911437988281\n",
      "loss4 :  0.021918391808867455 loss5 :  0.09793500602245331\n",
      "Iteration :  4  /  7\n",
      "loss :  0.018962478265166283\n",
      "lossl :  0.0 loss1 :  0.0028373717796057463 loss2 :  0.0034652710892260075 loss3 :  0.0012935638660565019\n",
      "loss4 :  0.005775260739028454 loss5 :  0.005591011140495539\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07656688988208771\n",
      "lossl :  0.0 loss1 :  0.004874038510024548 loss2 :  0.020761776715517044 loss3 :  0.01721210405230522\n",
      "loss4 :  0.015113020315766335 loss5 :  0.018605947494506836\n",
      "time taken :  0.16345977783203125\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 19/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.030632639303803444\n",
      "lossl :  0.0 loss1 :  0.004208469297736883 loss2 :  0.011369466781616211 loss3 :  0.0011391639709472656\n",
      "loss4 :  0.011809920892119408 loss5 :  0.002105617430061102\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03167085722088814\n",
      "lossl :  2.059936559817288e-05 loss1 :  0.0015185356605798006 loss2 :  0.007543993182480335 loss3 :  0.005884218029677868\n",
      "loss4 :  0.004704094026237726 loss5 :  0.011999416165053844\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08236213028430939\n",
      "lossl :  0.0 loss1 :  0.022174786776304245 loss2 :  0.0028222084511071444 loss3 :  0.028798390179872513\n",
      "loss4 :  0.0023906708229333162 loss5 :  0.026176070794463158\n",
      "time taken :  0.558067798614502\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05744190514087677\n",
      "lossl :  0.0 loss1 :  0.00109186174813658 loss2 :  0.020075703039765358 loss3 :  0.004707240965217352\n",
      "loss4 :  0.017537593841552734 loss5 :  0.014029502868652344\n",
      "Iteration :  4  /  7\n",
      "loss :  0.275725781917572\n",
      "lossl :  0.0 loss1 :  0.04154515266418457 loss2 :  0.07487626373767853 loss3 :  0.009347247891128063\n",
      "loss4 :  0.017239952459931374 loss5 :  0.13271717727184296\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006384563632309437\n",
      "lossl :  0.0 loss1 :  0.001901340438053012 loss2 :  0.0003777504025492817 loss3 :  0.003405952360481024\n",
      "loss4 :  0.0003124237118754536 loss5 :  0.0003870963992085308\n",
      "time taken :  0.16490411758422852\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 20/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.07947015762329102\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.0002993583620991558 loss2 :  0.019158268347382545 loss3 :  0.04110908508300781\n",
      "loss4 :  0.01092996634542942 loss5 :  0.007972145453095436\n",
      "Iteration :  4  /  7\n",
      "loss :  0.038224101066589355\n",
      "lossl :  0.0 loss1 :  0.021729469299316406 loss2 :  0.0013455391163006425 loss3 :  0.0022389411460608244\n",
      "loss4 :  0.012038493528962135 loss5 :  0.0008716583251953125\n",
      "Iteration :  7  /  7\n",
      "loss :  0.04247903451323509\n",
      "lossl :  2.956390289909905e-06 loss1 :  0.001947689102962613 loss2 :  0.012584495358169079 loss3 :  0.018831919878721237\n",
      "loss4 :  0.00018396376981399953 loss5 :  0.008928013034164906\n",
      "time taken :  0.5593047142028809\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1642247885465622\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.001256752060726285 loss2 :  0.14910265803337097 loss3 :  0.00081548688467592\n",
      "loss4 :  0.002670240355655551 loss5 :  0.010379457846283913\n",
      "Iteration :  4  /  7\n",
      "loss :  0.05059027671813965\n",
      "lossl :  0.0 loss1 :  0.0009847164619714022 loss2 :  0.026221800595521927 loss3 :  0.0023605346214026213\n",
      "loss4 :  0.011900472454726696 loss5 :  0.009122753515839577\n",
      "Iteration :  7  /  7\n",
      "loss :  0.09967222064733505\n",
      "lossl :  0.0 loss1 :  0.008299732580780983 loss2 :  0.0033439635299146175 loss3 :  0.02175912819802761\n",
      "loss4 :  0.02146434783935547 loss5 :  0.044805049896240234\n",
      "time taken :  0.19525837898254395\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 21/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.015877747908234596\n",
      "lossl :  0.0 loss1 :  8.506774611305445e-05 loss2 :  0.00027065275935456157 loss3 :  0.002915668534114957\n",
      "loss4 :  0.001998805906623602 loss5 :  0.010607552714645863\n",
      "Iteration :  4  /  7\n",
      "loss :  0.008173179812729359\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0003531455877237022 loss2 :  0.0002002716064453125 loss3 :  0.006596088409423828\n",
      "loss4 :  0.0003486633358988911 loss5 :  0.0006748199230059981\n",
      "Iteration :  7  /  7\n",
      "loss :  0.24105200171470642\n",
      "lossl :  6.86645489622606e-06 loss1 :  0.0017130852211266756 loss2 :  0.007450723554939032 loss3 :  0.0214201919734478\n",
      "loss4 :  0.01049494743347168 loss5 :  0.1999661922454834\n",
      "time taken :  0.5823559761047363\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.08791390061378479\n",
      "lossl :  0.0 loss1 :  0.0023592947982251644 loss2 :  0.00054168701171875 loss3 :  0.02391519583761692\n",
      "loss4 :  0.017254352569580078 loss5 :  0.04384336620569229\n",
      "Iteration :  4  /  7\n",
      "loss :  0.02342100255191326\n",
      "lossl :  0.0 loss1 :  0.00023789405531715602 loss2 :  0.003488826798275113 loss3 :  0.010742520913481712\n",
      "loss4 :  0.005400371737778187 loss5 :  0.003551387693732977\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4302420914173126\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.007563305087387562 loss2 :  0.08055801689624786 loss3 :  0.022883128374814987\n",
      "loss4 :  0.26433834433555603 loss5 :  0.05489911884069443\n",
      "time taken :  0.16997766494750977\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 22/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.036747027188539505\n",
      "lossl :  0.0 loss1 :  0.030851174145936966 loss2 :  0.0035118102096021175 loss3 :  0.0017342090141028166\n",
      "loss4 :  0.0001104354887502268 loss5 :  0.0005393981700763106\n",
      "Iteration :  4  /  7\n",
      "loss :  0.011663008481264114\n",
      "lossl :  0.0 loss1 :  0.004530477337539196 loss2 :  0.002448558807373047 loss3 :  0.0007377624278888106\n",
      "loss4 :  0.00015354156494140625 loss5 :  0.003792667295783758\n",
      "Iteration :  7  /  7\n",
      "loss :  0.02071075513958931\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.00037059784517623484 loss2 :  0.003993988037109375 loss3 :  0.0006505966302938759\n",
      "loss4 :  0.000790500664152205 loss5 :  0.014903736300766468\n",
      "time taken :  0.5698285102844238\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.02743043750524521\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.006447219755500555 loss2 :  0.001696681953035295 loss3 :  0.008975028991699219\n",
      "loss4 :  0.006165408995002508 loss5 :  0.004145908169448376\n",
      "Iteration :  4  /  7\n",
      "loss :  0.02870483510196209\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00029749871464446187 loss2 :  0.00904536247253418 loss3 :  0.005312156863510609\n",
      "loss4 :  0.008364629931747913 loss5 :  0.00568499555811286\n",
      "Iteration :  7  /  7\n",
      "loss :  0.23007825016975403\n",
      "lossl :  0.0 loss1 :  0.0009707451099529862 loss2 :  0.21386072039604187 loss3 :  0.003961467649787664\n",
      "loss4 :  0.0064108846709132195 loss5 :  0.00487442035228014\n",
      "time taken :  0.16740703582763672\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 23/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.006131649017333984\n",
      "lossl :  0.0 loss1 :  2.937316821771674e-05 loss2 :  0.001233768416568637 loss3 :  5.7697296142578125e-05\n",
      "loss4 :  0.0032356262672692537 loss5 :  0.0015751838218420744\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0036916257813572884\n",
      "lossl :  0.0 loss1 :  7.22885160939768e-05 loss2 :  0.002088689710944891 loss3 :  0.0008658409351482987\n",
      "loss4 :  0.0001260757417185232 loss5 :  0.000538730644620955\n",
      "Iteration :  7  /  7\n",
      "loss :  1.8835723400115967\n",
      "lossl :  7.629394644936838e-07 loss1 :  0.0005156517145223916 loss2 :  0.3139309287071228 loss3 :  0.5425616502761841\n",
      "loss4 :  0.6000921130180359 loss5 :  0.4264712333679199\n",
      "time taken :  0.5666608810424805\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09041688591241837\n",
      "lossl :  0.0 loss1 :  0.001055812812410295 loss2 :  0.07260014861822128 loss3 :  0.0072057247161865234\n",
      "loss4 :  0.0071832179091870785 loss5 :  0.0023719787131994963\n",
      "Iteration :  4  /  7\n",
      "loss :  0.27646762132644653\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0022791861556470394 loss2 :  0.07985682785511017 loss3 :  0.05160980299115181\n",
      "loss4 :  0.05407071113586426 loss5 :  0.08865070343017578\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08784839510917664\n",
      "lossl :  0.0 loss1 :  0.0018685341347008944 loss2 :  0.007558345794677734 loss3 :  0.039148829877376556\n",
      "loss4 :  0.0041977884247899055 loss5 :  0.03507490083575249\n",
      "time taken :  0.16826319694519043\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 24/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.23436686396598816\n",
      "lossl :  2.86102294921875e-06 loss1 :  0.06395311653614044 loss2 :  0.09580111503601074 loss3 :  0.0020321845076978207\n",
      "loss4 :  0.062374211847782135 loss5 :  0.010203361511230469\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03719200938940048\n",
      "lossl :  0.0 loss1 :  0.00030493736267089844 loss2 :  0.0004867553652729839 loss3 :  0.03570585325360298\n",
      "loss4 :  0.0001468658447265625 loss5 :  0.0005475998041220009\n",
      "Iteration :  7  /  7\n",
      "loss :  0.06680217385292053\n",
      "lossl :  0.0 loss1 :  0.011696052737534046 loss2 :  0.04627389833331108 loss3 :  0.0030596733558923006\n",
      "loss4 :  0.002270221710205078 loss5 :  0.003502321196720004\n",
      "time taken :  0.5888543128967285\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  4.234956741333008\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00941452942788601 loss2 :  2.0010995864868164 loss3 :  0.052772440016269684\n",
      "loss4 :  1.2419099807739258 loss5 :  0.9297599792480469\n",
      "Iteration :  4  /  7\n",
      "loss :  0.021348856389522552\n",
      "lossl :  0.0 loss1 :  0.0014022350078448653 loss2 :  0.0028905868530273438 loss3 :  0.00413060188293457\n",
      "loss4 :  0.005229091737419367 loss5 :  0.007696342654526234\n",
      "Iteration :  7  /  7\n",
      "loss :  0.6822783350944519\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0007761955494061112 loss2 :  0.42425331473350525 loss3 :  0.019057368859648705\n",
      "loss4 :  0.1309693157672882 loss5 :  0.10722176730632782\n",
      "time taken :  0.16838812828063965\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 25/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.00953969918191433\n",
      "lossl :  0.0 loss1 :  0.0001585006684763357 loss2 :  0.0006614684825763106 loss3 :  0.0005465507274493575\n",
      "loss4 :  0.00439839344471693 loss5 :  0.003774785902351141\n",
      "Iteration :  4  /  7\n",
      "loss :  0.13096052408218384\n",
      "lossl :  0.0 loss1 :  0.0005167961353436112 loss2 :  0.0154334781691432 loss3 :  0.009102916345000267\n",
      "loss4 :  0.0063779354095458984 loss5 :  0.09952940791845322\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05053424835205078\n",
      "lossl :  0.0 loss1 :  0.0011906623840332031 loss2 :  0.007166385650634766 loss3 :  0.012991237454116344\n",
      "loss4 :  0.027358055114746094 loss5 :  0.0018279075156897306\n",
      "time taken :  0.5698399543762207\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  3.458674669265747\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.0012532233959063888 loss2 :  1.2083921432495117 loss3 :  0.6292517781257629\n",
      "loss4 :  1.2822266817092896 loss5 :  0.3375503718852997\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1373288631439209\n",
      "lossl :  0.0 loss1 :  0.0010798930888995528 loss2 :  0.007485580630600452 loss3 :  0.009423350915312767\n",
      "loss4 :  0.07254095375537872 loss5 :  0.04679908603429794\n",
      "Iteration :  7  /  7\n",
      "loss :  4.3903303146362305\n",
      "lossl :  0.0 loss1 :  0.1265489161014557 loss2 :  1.2704650163650513 loss3 :  0.5365306735038757\n",
      "loss4 :  0.9293369054794312 loss5 :  1.5274488925933838\n",
      "time taken :  0.18133544921875\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 26/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  3.5872178077697754\n",
      "lossl :  0.0 loss1 :  0.0007075309986248612 loss2 :  1.2724876403808594 loss3 :  0.9308426976203918\n",
      "loss4 :  0.5444107055664062 loss5 :  0.8387691378593445\n",
      "Iteration :  4  /  7\n",
      "loss :  0.16686362028121948\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0035414695739746094 loss2 :  0.0018820762634277344 loss3 :  0.0361025333404541\n",
      "loss4 :  0.02277832105755806 loss5 :  0.1025589257478714\n",
      "Iteration :  7  /  7\n",
      "loss :  0.6917411088943481\n",
      "lossl :  2.288818359375e-05 loss1 :  0.25745588541030884 loss2 :  0.08594777435064316 loss3 :  0.019385671243071556\n",
      "loss4 :  0.2623773217201233 loss5 :  0.06655154377222061\n",
      "time taken :  0.5777463912963867\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  4.067067623138428\n",
      "lossl :  0.0 loss1 :  0.0051902770064771175 loss2 :  1.2493877410888672 loss3 :  1.3124326467514038\n",
      "loss4 :  1.1748173236846924 loss5 :  0.32523974776268005\n",
      "Iteration :  4  /  7\n",
      "loss :  5.537447452545166\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.001603794051334262 loss2 :  2.0700888633728027 loss3 :  0.29251009225845337\n",
      "loss4 :  1.5492141246795654 loss5 :  1.624030351638794\n",
      "Iteration :  7  /  7\n",
      "loss :  3.6900534629821777\n",
      "lossl :  0.0 loss1 :  0.04692220687866211 loss2 :  1.4766926765441895 loss3 :  0.04171581193804741\n",
      "loss4 :  1.4485759735107422 loss5 :  0.6761466264724731\n",
      "time taken :  0.17633295059204102\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 27/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2132447361946106\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.1676381379365921 loss2 :  0.0005964279407635331 loss3 :  0.0025489807594567537\n",
      "loss4 :  0.0002978324773721397 loss5 :  0.04216327518224716\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007651090621948242\n",
      "lossl :  0.0 loss1 :  0.004516506101936102 loss2 :  0.0007684707525186241 loss3 :  0.0014262199401855469\n",
      "loss4 :  0.00024862290592864156 loss5 :  0.000691270804964006\n",
      "Iteration :  7  /  7\n",
      "loss :  0.2937108278274536\n",
      "lossl :  5.0067901611328125e-05 loss1 :  0.012142729945480824 loss2 :  0.010914802551269531 loss3 :  0.1366276741027832\n",
      "loss4 :  0.06095423549413681 loss5 :  0.0730213150382042\n",
      "time taken :  0.5761504173278809\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  2.1107072830200195\n",
      "lossl :  0.0 loss1 :  0.24697205424308777 loss2 :  0.003305625868961215 loss3 :  0.5506404638290405\n",
      "loss4 :  0.7624596953392029 loss5 :  0.5473294258117676\n",
      "Iteration :  4  /  7\n",
      "loss :  0.32134273648262024\n",
      "lossl :  0.0 loss1 :  0.0006735801580362022 loss2 :  0.007558250334113836 loss3 :  0.00838613510131836\n",
      "loss4 :  0.25650501251220703 loss5 :  0.04821977764368057\n",
      "Iteration :  7  /  7\n",
      "loss :  1.2316205501556396\n",
      "lossl :  0.0 loss1 :  0.050135038793087006 loss2 :  0.5760295987129211 loss3 :  0.44031375646591187\n",
      "loss4 :  0.1612054407596588 loss5 :  0.003936767578125\n",
      "time taken :  0.1813662052154541\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 28/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2936098277568817\n",
      "lossl :  1.049041748046875e-05 loss1 :  0.1301807463169098 loss2 :  0.04691624641418457 loss3 :  0.08591878414154053\n",
      "loss4 :  0.006525611970573664 loss5 :  0.024057960137724876\n",
      "Iteration :  4  /  7\n",
      "loss :  0.05314178019762039\n",
      "lossl :  6.10351571594947e-06 loss1 :  0.0019306183094158769 loss2 :  0.0039539337158203125 loss3 :  0.020472144708037376\n",
      "loss4 :  0.0017191886436194181 loss5 :  0.025059795007109642\n",
      "Iteration :  7  /  7\n",
      "loss :  0.054835036396980286\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0020210265647619963 loss2 :  0.03923463821411133 loss3 :  0.004193496890366077\n",
      "loss4 :  0.0020244598854333162 loss5 :  0.007361030671745539\n",
      "time taken :  0.5793213844299316\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.9591473340988159\n",
      "lossl :  0.0 loss1 :  0.07887449115514755 loss2 :  0.005123042967170477 loss3 :  0.2578462064266205\n",
      "loss4 :  0.30387210845947266 loss5 :  0.3134315013885498\n",
      "Iteration :  4  /  7\n",
      "loss :  2.0235226154327393\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.00015897750563453883 loss2 :  1.0794694423675537 loss3 :  0.5462495684623718\n",
      "loss4 :  0.369800329208374 loss5 :  0.027843665331602097\n",
      "Iteration :  7  /  7\n",
      "loss :  0.47516533732414246\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.004798316862434149 loss2 :  0.006625366397202015 loss3 :  0.2896786630153656\n",
      "loss4 :  0.14668336510658264 loss5 :  0.027379322797060013\n",
      "time taken :  0.18098211288452148\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 29/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.9279652833938599\n",
      "lossl :  3.643035961431451e-05 loss1 :  0.13478679955005646 loss2 :  0.31008774042129517 loss3 :  0.11871476471424103\n",
      "loss4 :  0.09145955741405487 loss5 :  0.2728799283504486\n",
      "Iteration :  4  /  7\n",
      "loss :  2.651782512664795\n",
      "lossl :  3.337860107421875e-06 loss1 :  0.00046138762263581157 loss2 :  0.4350976347923279 loss3 :  0.7394350171089172\n",
      "loss4 :  0.9447342157363892 loss5 :  0.5320510268211365\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0645836889743805\n",
      "lossl :  1.430511474609375e-06 loss1 :  0.035539913922548294 loss2 :  0.018982410430908203 loss3 :  0.004586505703628063\n",
      "loss4 :  0.0003849029599223286 loss5 :  0.005088520236313343\n",
      "time taken :  0.596949577331543\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.3777492046356201\n",
      "lossl :  0.0 loss1 :  0.02129392698407173 loss2 :  0.0006959915044717491 loss3 :  0.20323514938354492\n",
      "loss4 :  0.09733748435974121 loss5 :  0.05518665164709091\n",
      "Iteration :  4  /  7\n",
      "loss :  0.28494998812675476\n",
      "lossl :  0.0 loss1 :  0.0007794380071572959 loss2 :  0.03392023965716362 loss3 :  0.006632995791733265\n",
      "loss4 :  0.23760171234607697 loss5 :  0.006015586666762829\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5404534339904785\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00032339096651412547 loss2 :  0.23538246750831604 loss3 :  0.19796457886695862\n",
      "loss4 :  0.10359640419483185 loss5 :  0.0031864165794104338\n",
      "time taken :  0.1814899444580078\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 30/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.01306614838540554\n",
      "lossl :  1.144409225162235e-06 loss1 :  0.0012265205150470138 loss2 :  0.008418416604399681 loss3 :  0.0011358261108398438\n",
      "loss4 :  0.00045680999755859375 loss5 :  0.0018274306785315275\n",
      "Iteration :  4  /  7\n",
      "loss :  0.008086776360869408\n",
      "lossl :  0.0 loss1 :  0.00014009475125931203 loss2 :  0.004388236906379461 loss3 :  0.0009034156682901084\n",
      "loss4 :  0.00169963832013309 loss5 :  0.0009553909185342491\n",
      "Iteration :  7  /  7\n",
      "loss :  0.2894834578037262\n",
      "lossl :  0.0 loss1 :  0.14995737373828888 loss2 :  0.013666200451552868 loss3 :  0.0952790230512619\n",
      "loss4 :  0.012738848105072975 loss5 :  0.01784200593829155\n",
      "time taken :  0.5803463459014893\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03610701486468315\n",
      "lossl :  0.0 loss1 :  0.01135926228016615 loss2 :  0.002784633543342352 loss3 :  0.01245412789285183\n",
      "loss4 :  0.0029172897338867188 loss5 :  0.006591701414436102\n",
      "Iteration :  4  /  7\n",
      "loss :  0.029374171048402786\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0012472153175622225 loss2 :  0.004657840821892023 loss3 :  0.0007719040149822831\n",
      "loss4 :  0.002494716551154852 loss5 :  0.0202023983001709\n",
      "Iteration :  7  /  7\n",
      "loss :  0.04993901401758194\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0024220466148108244 loss2 :  0.005968952085822821 loss3 :  0.012295866385102272\n",
      "loss4 :  0.010840797796845436 loss5 :  0.01841106452047825\n",
      "time taken :  0.16640496253967285\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 31/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01123590487986803\n",
      "lossl :  0.0 loss1 :  0.0005273818969726562 loss2 :  0.0007349968072958291 loss3 :  0.002795696258544922\n",
      "loss4 :  0.007062149234116077 loss5 :  0.00011568069749046117\n",
      "Iteration :  4  /  7\n",
      "loss :  0.27473485469818115\n",
      "lossl :  3.051757857974735e-06 loss1 :  0.006771564483642578 loss2 :  0.07304789870977402 loss3 :  0.014686107635498047\n",
      "loss4 :  0.09031061828136444 loss5 :  0.08991561084985733\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03461041674017906\n",
      "lossl :  0.0 loss1 :  0.0005338668706826866 loss2 :  0.025723982602357864 loss3 :  0.006482887081801891\n",
      "loss4 :  0.0010983466636389494 loss5 :  0.0007713317754678428\n",
      "time taken :  0.5616068840026855\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.9739876985549927\n",
      "lossl :  0.0 loss1 :  0.009979915805161 loss2 :  0.2432771623134613 loss3 :  0.25247517228126526\n",
      "loss4 :  0.037584878504276276 loss5 :  0.4306705594062805\n",
      "Iteration :  4  /  7\n",
      "loss :  0.3497649133205414\n",
      "lossl :  0.0 loss1 :  0.02324371412396431 loss2 :  0.0003215789911337197 loss3 :  0.15909676253795624\n",
      "loss4 :  0.12669292092323303 loss5 :  0.04040994495153427\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01863112486898899\n",
      "lossl :  0.0 loss1 :  0.0030846118461340666 loss2 :  0.0029739378951489925 loss3 :  0.00806436501443386\n",
      "loss4 :  0.0012983322376385331 loss5 :  0.003209877060726285\n",
      "time taken :  0.16622495651245117\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 32/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.26405346393585205\n",
      "lossl :  0.0 loss1 :  0.002463722135871649 loss2 :  0.03229217603802681 loss3 :  0.06665267795324326\n",
      "loss4 :  0.1525527983903885 loss5 :  0.010092067532241344\n",
      "Iteration :  4  /  7\n",
      "loss :  0.11616923660039902\n",
      "lossl :  1.716613724056515e-06 loss1 :  0.0004447936953511089 loss2 :  0.029676150530576706 loss3 :  0.05028402805328369\n",
      "loss4 :  0.026624441146850586 loss5 :  0.009138107299804688\n",
      "Iteration :  7  /  7\n",
      "loss :  0.43719160556793213\n",
      "lossl :  3.261566234868951e-05 loss1 :  0.04228515550494194 loss2 :  0.013975238427519798 loss3 :  0.1504453718662262\n",
      "loss4 :  0.008224964141845703 loss5 :  0.2222282439470291\n",
      "time taken :  0.5646722316741943\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.10059256851673126\n",
      "lossl :  0.0 loss1 :  0.000978183699771762 loss2 :  0.025852013379335403 loss3 :  0.005970859434455633\n",
      "loss4 :  0.04495429992675781 loss5 :  0.022837210446596146\n",
      "Iteration :  4  /  7\n",
      "loss :  0.012795448303222656\n",
      "lossl :  0.0 loss1 :  0.00047130585880950093 loss2 :  0.0034118653275072575 loss3 :  0.0013676643138751388\n",
      "loss4 :  0.003487491514533758 loss5 :  0.004057121463119984\n",
      "Iteration :  7  /  7\n",
      "loss :  0.3934462070465088\n",
      "lossl :  0.0 loss1 :  0.009919548407196999 loss2 :  0.12425832450389862 loss3 :  0.11273012310266495\n",
      "loss4 :  0.017722319811582565 loss5 :  0.1288158893585205\n",
      "time taken :  0.1662914752960205\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 33/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09161768108606339\n",
      "lossl :  0.0 loss1 :  0.014583873562514782 loss2 :  0.0041367532685399055 loss3 :  0.003280735109001398\n",
      "loss4 :  0.0018346786964684725 loss5 :  0.06778164207935333\n",
      "Iteration :  4  /  7\n",
      "loss :  0.014234066009521484\n",
      "lossl :  0.0 loss1 :  0.005718135740607977 loss2 :  0.006933689117431641 loss3 :  0.0005299568292684853\n",
      "loss4 :  0.0003440856817178428 loss5 :  0.0007081985240802169\n",
      "Iteration :  7  /  7\n",
      "loss :  0.26224076747894287\n",
      "lossl :  0.0 loss1 :  0.0029519081581383944 loss2 :  0.011696386151015759 loss3 :  0.0296705961227417\n",
      "loss4 :  0.2070786952972412 loss5 :  0.010843181982636452\n",
      "time taken :  0.5630862712860107\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.4830418825149536\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0005720138433389366 loss2 :  0.3475751280784607 loss3 :  0.4050964415073395\n",
      "loss4 :  0.0904824286699295 loss5 :  0.6393155455589294\n",
      "Iteration :  4  /  7\n",
      "loss :  0.027893351390957832\n",
      "lossl :  0.0 loss1 :  0.0016901015769690275 loss2 :  0.0041525838896632195 loss3 :  0.00260505685582757\n",
      "loss4 :  0.01522903423756361 loss5 :  0.004216575529426336\n",
      "Iteration :  7  /  7\n",
      "loss :  0.8771539926528931\n",
      "lossl :  0.0 loss1 :  0.00018949508375953883 loss2 :  0.2122064083814621 loss3 :  0.26267629861831665\n",
      "loss4 :  0.40027666091918945 loss5 :  0.0018051147926598787\n",
      "time taken :  0.1693737506866455\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 34/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.044869519770145416\n",
      "lossl :  0.0 loss1 :  0.0013031482230871916 loss2 :  0.03904471546411514 loss3 :  0.001702976180240512\n",
      "loss4 :  0.00025997162447310984 loss5 :  0.0025587081909179688\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03262071684002876\n",
      "lossl :  0.0 loss1 :  0.0004931449657306075 loss2 :  0.002612590789794922 loss3 :  0.007909774780273438\n",
      "loss4 :  0.010457992553710938 loss5 :  0.01114721316844225\n",
      "Iteration :  7  /  7\n",
      "loss :  0.04723067209124565\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.011358166113495827 loss2 :  0.010836267843842506 loss3 :  0.0007973670726642013\n",
      "loss4 :  0.002872562501579523 loss5 :  0.021366119384765625\n",
      "time taken :  0.560330867767334\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.0514975786209106\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.046492815017700195 loss2 :  0.00344676966778934 loss3 :  0.39458519220352173\n",
      "loss4 :  0.411710262298584 loss5 :  0.195262148976326\n",
      "Iteration :  4  /  7\n",
      "loss :  0.08579130470752716\n",
      "lossl :  0.0 loss1 :  0.00426406878978014 loss2 :  0.003170585725456476 loss3 :  0.014249849133193493\n",
      "loss4 :  0.04128532484173775 loss5 :  0.022821474820375443\n",
      "Iteration :  7  /  7\n",
      "loss :  0.3077602684497833\n",
      "lossl :  0.0 loss1 :  0.0016067505348473787 loss2 :  0.1766192466020584 loss3 :  0.09349465370178223\n",
      "loss4 :  0.028621386736631393 loss5 :  0.007418251130729914\n",
      "time taken :  0.1829369068145752\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 35/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.25597167015075684\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0010533332824707031 loss2 :  0.010226964950561523 loss3 :  0.005529451183974743\n",
      "loss4 :  0.01721477508544922 loss5 :  0.22194695472717285\n",
      "Iteration :  4  /  7\n",
      "loss :  2.8156306743621826\n",
      "lossl :  1.23977656585339e-06 loss1 :  0.10782794654369354 loss2 :  0.5263739824295044 loss3 :  0.40501099824905396\n",
      "loss4 :  0.8929437398910522 loss5 :  0.8834728002548218\n",
      "Iteration :  7  /  7\n",
      "loss :  0.030485104769468307\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0004105567932128906 loss2 :  0.0035174370277673006 loss3 :  0.016591167077422142\n",
      "loss4 :  0.006120014004409313 loss5 :  0.003845739411190152\n",
      "time taken :  0.5806643962860107\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1603911966085434\n",
      "lossl :  0.0 loss1 :  0.0032209395430982113 loss2 :  0.06097093224525452 loss3 :  0.027100944891572\n",
      "loss4 :  0.00810537301003933 loss5 :  0.0609930045902729\n",
      "Iteration :  4  /  7\n",
      "loss :  0.08424334973096848\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.0033903121948242188 loss2 :  0.031196212396025658 loss3 :  0.019055938348174095\n",
      "loss4 :  0.019013071432709694 loss5 :  0.011587333865463734\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0456356555223465\n",
      "lossl :  0.0 loss1 :  0.024944067001342773 loss2 :  0.005301809404045343 loss3 :  0.0017048835288733244\n",
      "loss4 :  0.012039232067763805 loss5 :  0.0016456603771075606\n",
      "time taken :  0.1705472469329834\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 36/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.554387629032135\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.007482290267944336 loss2 :  0.001377964043058455 loss3 :  0.007627964019775391\n",
      "loss4 :  0.012883806601166725 loss5 :  0.5250154137611389\n",
      "Iteration :  4  /  7\n",
      "loss :  0.05541066452860832\n",
      "lossl :  0.0 loss1 :  0.001481813145801425 loss2 :  0.0009048461797647178 loss3 :  0.02129802666604519\n",
      "loss4 :  0.023051071912050247 loss5 :  0.008674907498061657\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005623912438750267\n",
      "lossl :  0.0 loss1 :  2.2029877072782256e-05 loss2 :  0.00023193359083961695 loss3 :  0.0023317337036132812\n",
      "loss4 :  0.0018688201671466231 loss5 :  0.001169395400211215\n",
      "time taken :  0.5882325172424316\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.5707249641418457\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.015424823388457298 loss2 :  0.006376361940056086 loss3 :  0.23501968383789062\n",
      "loss4 :  0.21991023421287537 loss5 :  0.0939936637878418\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006539583206176758\n",
      "lossl :  0.0 loss1 :  0.001202058745548129 loss2 :  0.0016566276317462325 loss3 :  0.0019733428489416838\n",
      "loss4 :  0.00038776398287154734 loss5 :  0.0013197899097576737\n",
      "Iteration :  7  /  7\n",
      "loss :  0.016286898404359818\n",
      "lossl :  0.0 loss1 :  0.007533264346420765 loss2 :  0.0002193450927734375 loss3 :  0.006779050920158625\n",
      "loss4 :  0.0012901306618005037 loss5 :  0.0004651069757528603\n",
      "time taken :  0.1643996238708496\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 37/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1440686136484146\n",
      "lossl :  0.0 loss1 :  0.00037555693415924907 loss2 :  0.0015461922157555819 loss3 :  0.00943689327687025\n",
      "loss4 :  0.0002120971621479839 loss5 :  0.1324978768825531\n",
      "Iteration :  4  /  7\n",
      "loss :  0.40884315967559814\n",
      "lossl :  0.0 loss1 :  0.015706252306699753 loss2 :  0.0012410164345055819 loss3 :  0.00960073433816433\n",
      "loss4 :  0.341021865606308 loss5 :  0.04127330705523491\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08813633769750595\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0004981994861736894 loss2 :  0.00253639230504632 loss3 :  0.06009101867675781\n",
      "loss4 :  0.00802536029368639 loss5 :  0.016985082998871803\n",
      "time taken :  0.5567677021026611\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.4480016231536865\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.009915446862578392 loss2 :  0.0699094757437706 loss3 :  0.4764133393764496\n",
      "loss4 :  0.798143744468689 loss5 :  0.09361934661865234\n",
      "Iteration :  4  /  7\n",
      "loss :  0.011770844459533691\n",
      "lossl :  0.0 loss1 :  0.00034160615177825093 loss2 :  0.004471039865165949 loss3 :  0.002695751143619418\n",
      "loss4 :  0.002753830049186945 loss5 :  0.0015086174244061112\n",
      "Iteration :  7  /  7\n",
      "loss :  0.008925962261855602\n",
      "lossl :  0.0 loss1 :  0.0027250766288489103 loss2 :  0.000171661376953125 loss3 :  0.002889442490413785\n",
      "loss4 :  0.0012806892627850175 loss5 :  0.0018590927356854081\n",
      "time taken :  0.16447687149047852\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 38/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.8182886838912964\n",
      "lossl :  0.0 loss1 :  0.019455432891845703 loss2 :  0.005316972732543945 loss3 :  0.4692007005214691\n",
      "loss4 :  0.31265169382095337 loss5 :  0.01166391372680664\n",
      "Iteration :  4  /  7\n",
      "loss :  0.34960052371025085\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0011410713195800781 loss2 :  0.0015291214222088456 loss3 :  0.01330270804464817\n",
      "loss4 :  0.029073525220155716 loss5 :  0.3045538067817688\n",
      "Iteration :  7  /  7\n",
      "loss :  0.25967317819595337\n",
      "lossl :  0.0 loss1 :  0.036604881286621094 loss2 :  0.026553725823760033 loss3 :  0.08747100830078125\n",
      "loss4 :  0.0697452574968338 loss5 :  0.039298295974731445\n",
      "time taken :  0.5558004379272461\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008841323666274548\n",
      "lossl :  0.0 loss1 :  0.005160236265510321 loss2 :  0.00018758773512672633 loss3 :  0.0016521454090252519\n",
      "loss4 :  0.0015303611289709806 loss5 :  0.0003109932004008442\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00902466755360365\n",
      "lossl :  0.0 loss1 :  3.604888843256049e-05 loss2 :  0.006746816448867321 loss3 :  0.00029697417630814016\n",
      "loss4 :  0.001729774521663785 loss5 :  0.00021505355834960938\n",
      "Iteration :  7  /  7\n",
      "loss :  0.02644939534366131\n",
      "lossl :  0.0 loss1 :  0.0010611533652991056 loss2 :  0.0007204056018963456 loss3 :  0.0006451606750488281\n",
      "loss4 :  0.022166062146425247 loss5 :  0.0018566132057458162\n",
      "time taken :  0.165435791015625\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 39/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06092014163732529\n",
      "lossl :  9.536743306171047e-08 loss1 :  6.456374831032008e-05 loss2 :  0.02536463737487793 loss3 :  0.033460378646850586\n",
      "loss4 :  0.00024690627469681203 loss5 :  0.00178356165997684\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006660080049186945\n",
      "lossl :  0.0 loss1 :  0.00024003982252907008 loss2 :  0.00032167433528229594 loss3 :  0.002045917557552457\n",
      "loss4 :  0.00031642912654206157 loss5 :  0.0037360191345214844\n",
      "Iteration :  7  /  7\n",
      "loss :  0.17495965957641602\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.005795192904770374 loss2 :  0.026075173169374466 loss3 :  0.024845648556947708\n",
      "loss4 :  0.04967150837182999 loss5 :  0.06857176125049591\n",
      "time taken :  0.5598933696746826\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05425585061311722\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0031529427506029606 loss2 :  0.01750992052257061 loss3 :  0.009971141815185547\n",
      "loss4 :  0.0010174751514568925 loss5 :  0.02260408364236355\n",
      "Iteration :  4  /  7\n",
      "loss :  1.186802625656128\n",
      "lossl :  0.0 loss1 :  0.0003002166631631553 loss2 :  0.17187651991844177 loss3 :  0.35330015420913696\n",
      "loss4 :  0.6604113578796387 loss5 :  0.0009142875787802041\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03587593883275986\n",
      "lossl :  0.0 loss1 :  0.004297828767448664 loss2 :  0.0025327682960778475 loss3 :  0.004474020097404718\n",
      "loss4 :  0.023365210741758347 loss5 :  0.0012061118613928556\n",
      "time taken :  0.16467928886413574\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 40/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.16491812467575073\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.06231100484728813 loss2 :  0.005453777499496937 loss3 :  0.0031162737868726254\n",
      "loss4 :  0.015788841992616653 loss5 :  0.07824764400720596\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07526931911706924\n",
      "lossl :  0.0 loss1 :  0.012432861141860485 loss2 :  0.03965292125940323 loss3 :  0.003006076905876398\n",
      "loss4 :  0.019957637414336205 loss5 :  0.00021982192993164062\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1410176306962967\n",
      "lossl :  5.245208740234375e-06 loss1 :  0.003907537553459406 loss2 :  0.03161430358886719 loss3 :  0.029407262802124023\n",
      "loss4 :  0.06423673778772354 loss5 :  0.011846542358398438\n",
      "time taken :  0.5611250400543213\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004621696658432484\n",
      "lossl :  0.0 loss1 :  0.0008978843688964844 loss2 :  0.0007164001581259072 loss3 :  0.0012067795032635331\n",
      "loss4 :  0.0005792618030682206 loss5 :  0.0012213706504553556\n",
      "Iteration :  4  /  7\n",
      "loss :  0.9584041237831116\n",
      "lossl :  0.0 loss1 :  0.0010293007362633944 loss2 :  0.10886514186859131 loss3 :  0.48683756589889526\n",
      "loss4 :  0.3198052942752838 loss5 :  0.04186682775616646\n",
      "Iteration :  7  /  7\n",
      "loss :  1.1669026613235474\n",
      "lossl :  0.0 loss1 :  0.04709015041589737 loss2 :  0.17464284598827362 loss3 :  0.3319428563117981\n",
      "loss4 :  0.612557053565979 loss5 :  0.0006696701166220009\n",
      "time taken :  0.16420316696166992\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 41/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.32895639538764954\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.007242536637932062 loss2 :  0.2153468132019043 loss3 :  0.03270673751831055\n",
      "loss4 :  0.06328348815441132 loss5 :  0.010376596823334694\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2008924037218094\n",
      "lossl :  4.00543212890625e-05 loss1 :  0.0014801978832110763 loss2 :  0.017506027594208717 loss3 :  0.08836831897497177\n",
      "loss4 :  0.047124721109867096 loss5 :  0.04637308046221733\n",
      "Iteration :  7  /  7\n",
      "loss :  0.18062834441661835\n",
      "lossl :  3.051757857974735e-06 loss1 :  0.04135532304644585 loss2 :  0.009047126397490501 loss3 :  0.09809684753417969\n",
      "loss4 :  0.0007772445678710938 loss5 :  0.03134875372052193\n",
      "time taken :  0.56276535987854\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03394489362835884\n",
      "lossl :  0.0 loss1 :  0.0008596420520916581 loss2 :  0.0010724067687988281 loss3 :  0.0008034706115722656\n",
      "loss4 :  0.024994468316435814 loss5 :  0.006214904598891735\n",
      "Iteration :  4  /  7\n",
      "loss :  1.4803169965744019\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.04131145402789116 loss2 :  0.3143238425254822 loss3 :  0.5980771780014038\n",
      "loss4 :  0.4294363856315613 loss5 :  0.09716758877038956\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07786226272583008\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0003608703555073589 loss2 :  0.06312151253223419 loss3 :  0.0036406517028808594\n",
      "loss4 :  0.0031955719459801912 loss5 :  0.0075432779267430305\n",
      "time taken :  0.16591382026672363\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 42/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.646073043346405\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.00024118422879837453 loss2 :  0.28247642517089844 loss3 :  0.22812871634960175\n",
      "loss4 :  0.004030084703117609 loss5 :  0.13119621574878693\n",
      "Iteration :  4  /  7\n",
      "loss :  0.6388712525367737\n",
      "lossl :  1.430511474609375e-06 loss1 :  0.0022514343727380037 loss2 :  0.14561624825000763 loss3 :  0.002612209413200617\n",
      "loss4 :  0.48616427183151245 loss5 :  0.002225685166195035\n",
      "Iteration :  7  /  7\n",
      "loss :  0.041702937334775925\n",
      "lossl :  0.0 loss1 :  0.01570138894021511 loss2 :  0.0032063485123217106 loss3 :  0.01715707778930664\n",
      "loss4 :  0.00046663283137604594 loss5 :  0.005171489901840687\n",
      "time taken :  0.5597231388092041\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03487086296081543\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.010135030373930931 loss2 :  0.015060091391205788 loss3 :  0.00521540641784668\n",
      "loss4 :  0.0025840760208666325 loss5 :  0.0018757820362225175\n",
      "Iteration :  4  /  7\n",
      "loss :  0.5871925354003906\n",
      "lossl :  0.0 loss1 :  8.76426711329259e-05 loss2 :  0.1232232078909874 loss3 :  0.16035738587379456\n",
      "loss4 :  0.302485853433609 loss5 :  0.00103845598641783\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03719072416424751\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0005183219909667969 loss2 :  0.002069663954898715 loss3 :  0.006714439485222101\n",
      "loss4 :  0.015980148687958717 loss5 :  0.01190795935690403\n",
      "time taken :  0.16540265083312988\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 43/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.675030529499054\n",
      "lossl :  0.0 loss1 :  2.2602082026423886e-05 loss2 :  0.37828928232192993 loss3 :  0.050506703555583954\n",
      "loss4 :  0.09148969501256943 loss5 :  0.15472225844860077\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01578369177877903\n",
      "lossl :  1.106262243411038e-05 loss1 :  0.0010476112365722656 loss2 :  0.0022661208640784025 loss3 :  0.0007105827098712325\n",
      "loss4 :  0.003594684647396207 loss5 :  0.00815362948924303\n",
      "Iteration :  7  /  7\n",
      "loss :  0.043267153203487396\n",
      "lossl :  0.0 loss1 :  0.0005709648248739541 loss2 :  0.027477551251649857 loss3 :  0.0033271790016442537\n",
      "loss4 :  0.009563160128891468 loss5 :  0.0023283003829419613\n",
      "time taken :  0.5633668899536133\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.8371620774269104\n",
      "lossl :  0.0 loss1 :  0.0034474371932446957 loss2 :  0.2891162633895874 loss3 :  0.3217238783836365\n",
      "loss4 :  0.17118027806282043 loss5 :  0.05169420316815376\n",
      "Iteration :  4  /  7\n",
      "loss :  0.41826462745666504\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.00016622543625999242 loss2 :  0.07344841957092285 loss3 :  0.09329251945018768\n",
      "loss4 :  0.24318476021289825 loss5 :  0.008172321133315563\n",
      "Iteration :  7  /  7\n",
      "loss :  0.13641206920146942\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0361538901925087 loss2 :  0.013318419456481934 loss3 :  0.008382320404052734\n",
      "loss4 :  0.0013191222678869963 loss5 :  0.07723812758922577\n",
      "time taken :  0.16660141944885254\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 44/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.12622085213661194\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00023069381131790578 loss2 :  0.010314559563994408 loss3 :  0.07275581359863281\n",
      "loss4 :  0.006963491439819336 loss5 :  0.03595609590411186\n",
      "Iteration :  4  /  7\n",
      "loss :  0.343152791261673\n",
      "lossl :  2.098083541568485e-06 loss1 :  0.004854536149650812 loss2 :  0.04019131511449814 loss3 :  0.2472623884677887\n",
      "loss4 :  0.0005738258478231728 loss5 :  0.05026865005493164\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07360901683568954\n",
      "lossl :  0.0 loss1 :  0.007195377256721258 loss2 :  0.00867009162902832 loss3 :  0.051853083074092865\n",
      "loss4 :  0.0041564940474927425 loss5 :  0.001733970595523715\n",
      "time taken :  0.5609099864959717\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.023186970502138138\n",
      "lossl :  0.0 loss1 :  0.00018615722365211695 loss2 :  0.0012481689918786287 loss3 :  0.0014141083229333162\n",
      "loss4 :  0.007109927944839001 loss5 :  0.01322860736399889\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004349470604211092\n",
      "lossl :  0.0 loss1 :  0.0014825344551354647 loss2 :  0.00048351287841796875 loss3 :  0.0014484405983239412\n",
      "loss4 :  0.0006405830499716103 loss5 :  0.00029439927311614156\n",
      "Iteration :  7  /  7\n",
      "loss :  0.016095256432890892\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00025730131892487407 loss2 :  0.0015214920276775956 loss3 :  0.002162361051887274\n",
      "loss4 :  0.0008476257207803428 loss5 :  0.011306285858154297\n",
      "time taken :  0.1666727066040039\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 45/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.020109940320253372\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00016622543625999242 loss2 :  0.0020126341842114925 loss3 :  0.004715061280876398\n",
      "loss4 :  0.010553980246186256 loss5 :  0.0026618479751050472\n",
      "Iteration :  4  /  7\n",
      "loss :  0.18182209134101868\n",
      "lossl :  0.0003372192441020161 loss1 :  0.00061712262686342 loss2 :  0.04934690147638321 loss3 :  0.014953422360122204\n",
      "loss4 :  0.05969667434692383 loss5 :  0.05687074735760689\n",
      "Iteration :  7  /  7\n",
      "loss :  0.11225690692663193\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.013398027047514915 loss2 :  0.08661971241235733 loss3 :  0.007221317384392023\n",
      "loss4 :  0.004527091979980469 loss5 :  0.0004905700916424394\n",
      "time taken :  0.5639925003051758\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.4100923538208008\n",
      "lossl :  0.0 loss1 :  0.043466757982969284 loss2 :  0.052877046167850494 loss3 :  0.05301494523882866\n",
      "loss4 :  0.21228989958763123 loss5 :  0.04844369739294052\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2745969891548157\n",
      "lossl :  0.0 loss1 :  0.044510651379823685 loss2 :  0.0005620002630166709 loss3 :  0.08636923134326935\n",
      "loss4 :  0.02129373513162136 loss5 :  0.12186136096715927\n",
      "Iteration :  7  /  7\n",
      "loss :  0.43407684564590454\n",
      "lossl :  0.0 loss1 :  0.00017156600370071828 loss2 :  0.16619953513145447 loss3 :  0.16516241431236267\n",
      "loss4 :  0.061133503913879395 loss5 :  0.04140982776880264\n",
      "time taken :  0.1653909683227539\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 46/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.031774017959833145\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.0072170733474195 loss2 :  0.007286190986633301 loss3 :  0.004897975828498602\n",
      "loss4 :  0.00297374720685184 loss5 :  0.009398174472153187\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0216887928545475\n",
      "lossl :  0.0 loss1 :  0.0001655578671488911 loss2 :  0.012751531787216663 loss3 :  0.003677272703498602\n",
      "loss4 :  0.0026714324485510588 loss5 :  0.0024230002891272306\n",
      "Iteration :  7  /  7\n",
      "loss :  0.024822331964969635\n",
      "lossl :  4.963874744134955e-05 loss1 :  0.00029616354731842875 loss2 :  0.0013611793983727694 loss3 :  0.0031126022804528475\n",
      "loss4 :  0.0011712074046954513 loss5 :  0.018831539899110794\n",
      "time taken :  0.5709381103515625\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2966417968273163\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00010633468627929688 loss2 :  0.03734955936670303 loss3 :  0.042644597589969635\n",
      "loss4 :  0.21624203026294708 loss5 :  0.0002990722714457661\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010525799356400967\n",
      "lossl :  0.0 loss1 :  0.002098464872688055 loss2 :  0.0014514923095703125 loss3 :  0.0017580032581463456\n",
      "loss4 :  0.003417587373405695 loss5 :  0.0018002509605139494\n",
      "Iteration :  7  /  7\n",
      "loss :  0.04217381775379181\n",
      "lossl :  1.430511474609375e-06 loss1 :  0.0004199027898721397 loss2 :  0.00606117257848382 loss3 :  0.006212472915649414\n",
      "loss4 :  0.003123760223388672 loss5 :  0.0263550765812397\n",
      "time taken :  0.20015907287597656\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 47/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07005781680345535\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.00047245025052689016 loss2 :  0.009271621704101562 loss3 :  0.006151867099106312\n",
      "loss4 :  0.05214495584368706 loss5 :  0.0020159720443189144\n",
      "Iteration :  4  /  7\n",
      "loss :  0.7845251560211182\n",
      "lossl :  0.0 loss1 :  0.00022916794114280492 loss2 :  0.08203692734241486 loss3 :  0.0019481659401208162\n",
      "loss4 :  0.683285117149353 loss5 :  0.01702575758099556\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005224799737334251\n",
      "lossl :  3.814697322468419e-07 loss1 :  1.4591217222914565e-05 loss2 :  0.0002719879266805947 loss3 :  0.004254722502082586\n",
      "loss4 :  9.193420555675402e-05 loss5 :  0.0005911827320232987\n",
      "time taken :  0.5865809917449951\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.048523567616939545\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.0019899369217455387 loss2 :  0.003361177397891879 loss3 :  0.007052802946418524\n",
      "loss4 :  0.026396941393613815 loss5 :  0.009720707312226295\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1387893110513687\n",
      "lossl :  0.0 loss1 :  9.469986252952367e-05 loss2 :  0.05086207389831543 loss3 :  0.06080126762390137\n",
      "loss4 :  0.013977098278701305 loss5 :  0.013054179958999157\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4637534022331238\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.06033835560083389 loss2 :  0.04057781770825386 loss3 :  0.09148464351892471\n",
      "loss4 :  0.16394057869911194 loss5 :  0.10741162300109863\n",
      "time taken :  0.192765474319458\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 48/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09836509078741074\n",
      "lossl :  0.0 loss1 :  3.44276413670741e-05 loss2 :  0.030225157737731934 loss3 :  0.024297380819916725\n",
      "loss4 :  0.04346213489770889 loss5 :  0.0003459930303506553\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004359340760856867\n",
      "lossl :  0.0 loss1 :  0.0011852264869958162 loss2 :  0.0019740103743970394 loss3 :  0.0004994392511434853\n",
      "loss4 :  0.0002548217889852822 loss5 :  0.0004458427429199219\n",
      "Iteration :  7  /  7\n",
      "loss :  0.015552854165434837\n",
      "lossl :  7.22885160939768e-05 loss1 :  0.0010584354167804122 loss2 :  0.0027794837951660156 loss3 :  0.005137824919074774\n",
      "loss4 :  0.005668163299560547 loss5 :  0.0008366584661416709\n",
      "time taken :  0.5758523941040039\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.020884133875370026\n",
      "lossl :  0.0 loss1 :  0.00021047591872047633 loss2 :  0.006923866458237171 loss3 :  0.0018449783092364669\n",
      "loss4 :  0.004899406339973211 loss5 :  0.0070054056122899055\n",
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      "loss :  0.07845516502857208\n",
      "lossl :  0.0 loss1 :  0.030176306143403053 loss2 :  0.0031782626174390316 loss3 :  0.006351375486701727\n",
      "loss4 :  0.01633911207318306 loss5 :  0.022410105913877487\n",
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      "loss :  0.012140274047851562\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0002970695495605469 loss2 :  0.0015676498878747225 loss3 :  0.0007416725275106728\n",
      "loss4 :  0.0014475822681561112 loss5 :  0.008086013607680798\n",
      "time taken :  0.18118548393249512\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 49/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.19727492332458496\n",
      "lossl :  0.0 loss1 :  0.05624813959002495 loss2 :  0.005538272671401501 loss3 :  0.008763122372329235\n",
      "loss4 :  0.004330635070800781 loss5 :  0.12239475548267365\n",
      "Iteration :  4  /  7\n",
      "loss :  0.032300759106874466\n",
      "lossl :  0.0 loss1 :  9.593963477527723e-05 loss2 :  0.0007015228038653731 loss3 :  0.02828054502606392\n",
      "loss4 :  0.0013294219970703125 loss5 :  0.0018933296669274569\n",
      "Iteration :  7  /  7\n",
      "loss :  0.048780154436826706\n",
      "lossl :  0.0 loss1 :  0.0003909110964741558 loss2 :  0.037660788744688034 loss3 :  0.0005857467767782509\n",
      "loss4 :  0.00034389496431685984 loss5 :  0.009798812679946423\n",
      "time taken :  0.6134216785430908\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.10995855182409286\n",
      "lossl :  0.0 loss1 :  7.972717139637098e-05 loss2 :  0.027757931500673294 loss3 :  0.020537089556455612\n",
      "loss4 :  0.055180929601192474 loss5 :  0.006402873899787664\n",
      "Iteration :  4  /  7\n",
      "loss :  0.012676429003477097\n",
      "lossl :  0.0 loss1 :  0.0008619308355264366 loss2 :  0.00017709731764625758 loss3 :  0.00147161481436342\n",
      "loss4 :  0.00977468490600586 loss5 :  0.0003911018429789692\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009499263018369675\n",
      "lossl :  1.8119811784345075e-06 loss1 :  0.0005987167241983116 loss2 :  0.0022877692244946957 loss3 :  0.002094173338264227\n",
      "loss4 :  0.002251911209896207 loss5 :  0.0022648810409009457\n",
      "time taken :  0.18120932579040527\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 50/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.003501319792121649\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0008079528925009072 loss2 :  0.00030260084895417094 loss3 :  0.00046367646427825093\n",
      "loss4 :  0.0006361007690429688 loss5 :  0.0012904166942462325\n",
      "Iteration :  4  /  7\n",
      "loss :  0.018465280532836914\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00042591095552779734 loss2 :  0.0006734848138876259 loss3 :  0.004423809237778187\n",
      "loss4 :  0.0024216175079345703 loss5 :  0.01052026730030775\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01787109300494194\n",
      "lossl :  0.0 loss1 :  0.002154827117919922 loss2 :  0.00880212802439928 loss3 :  0.0051513672806322575\n",
      "loss4 :  0.0007340431329794228 loss5 :  0.0010287284385412931\n",
      "time taken :  0.5859627723693848\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.22515809535980225\n",
      "lossl :  0.0 loss1 :  0.02497110329568386 loss2 :  0.07889039814472198 loss3 :  0.03368673473596573\n",
      "loss4 :  0.06255707889795303 loss5 :  0.025052785873413086\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007386588491499424\n",
      "lossl :  0.0 loss1 :  5.302429053699598e-05 loss2 :  0.0012964248890057206 loss3 :  0.0006050110096111894\n",
      "loss4 :  0.0033576011192053556 loss5 :  0.0020745277870446444\n",
      "Iteration :  7  /  7\n",
      "loss :  0.010415364056825638\n",
      "lossl :  0.0 loss1 :  0.00148944859392941 loss2 :  0.0004585265996865928 loss3 :  0.0020208358764648438\n",
      "loss4 :  0.006030273623764515 loss5 :  0.0004162788391113281\n",
      "time taken :  0.18066930770874023\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 51/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005241108126938343\n",
      "lossl :  0.0 loss1 :  0.0001659393310546875 loss2 :  0.0013764381874352694 loss3 :  0.0007750511285848916\n",
      "loss4 :  0.002877902938053012 loss5 :  4.57763671875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0920766294002533\n",
      "lossl :  5.722046125811175e-07 loss1 :  7.677078247070312e-05 loss2 :  0.005463838577270508 loss3 :  0.013915491290390491\n",
      "loss4 :  0.002887725830078125 loss5 :  0.06973223388195038\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05114183574914932\n",
      "lossl :  0.0 loss1 :  0.008786821737885475 loss2 :  0.03460192680358887 loss3 :  0.0008064269786700606\n",
      "loss4 :  0.005885124206542969 loss5 :  0.0010615348583087325\n",
      "time taken :  0.5740165710449219\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.02020750194787979\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.006831836886703968 loss2 :  0.0018714905017986894 loss3 :  0.0033178329467773438\n",
      "loss4 :  0.0005187034839764237 loss5 :  0.007666587829589844\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005064773373305798\n",
      "lossl :  0.0 loss1 :  0.0004595756472554058 loss2 :  0.0037882805336266756 loss3 :  0.0002849578741006553\n",
      "loss4 :  0.00040750502375885844 loss5 :  0.00012445449829101562\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004362678620964289\n",
      "lossl :  0.0 loss1 :  2.8133392333984375e-05 loss2 :  0.0010603904956951737 loss3 :  0.00015001297288108617\n",
      "loss4 :  0.0012718200450763106 loss5 :  0.001852321671321988\n",
      "time taken :  0.17676520347595215\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 52/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.023000575602054596\n",
      "lossl :  0.0 loss1 :  0.00176153180655092 loss2 :  0.002180385636165738 loss3 :  0.0004288673517294228\n",
      "loss4 :  0.01390929240733385 loss5 :  0.004720496945083141\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004244661424309015\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0002700328768696636 loss2 :  0.0003945350763387978 loss3 :  9.498596045887098e-05\n",
      "loss4 :  0.0006613731384277344 loss5 :  0.0028235435020178556\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03689198195934296\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.0003349781036376953 loss2 :  0.005076312925666571 loss3 :  0.004021167755126953\n",
      "loss4 :  0.0203673355281353 loss5 :  0.007091522216796875\n",
      "time taken :  0.5723397731781006\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.035819388926029205\n",
      "lossl :  0.0 loss1 :  0.005735492799431086 loss2 :  0.003900766372680664 loss3 :  0.0036724091041833162\n",
      "loss4 :  0.005225753877311945 loss5 :  0.01728496514260769\n",
      "Iteration :  4  /  7\n",
      "loss :  0.09730910509824753\n",
      "lossl :  0.0 loss1 :  0.0031915665604174137 loss2 :  0.00022153854661155492 loss3 :  0.07070913165807724\n",
      "loss4 :  0.022765446454286575 loss5 :  0.0004214286745991558\n",
      "Iteration :  7  /  7\n",
      "loss :  0.017917443066835403\n",
      "lossl :  0.0 loss1 :  0.000331878662109375 loss2 :  0.0029430389404296875 loss3 :  0.0028857230208814144\n",
      "loss4 :  0.002171850297600031 loss5 :  0.009584951214492321\n",
      "time taken :  0.17782855033874512\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 53/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.6367419958114624\n",
      "lossl :  0.0 loss1 :  3.156661841785535e-05 loss2 :  0.004597854800522327 loss3 :  0.0015903472667559981\n",
      "loss4 :  0.04760418087244034 loss5 :  0.5829180479049683\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004653739742934704\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0010722160805016756 loss2 :  7.686614844715223e-05 loss3 :  0.0013371467357501388\n",
      "loss4 :  0.00031538010807707906 loss5 :  0.001851844834163785\n",
      "Iteration :  7  /  7\n",
      "loss :  0.21749766170978546\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.17854157090187073 loss2 :  0.0011462211841717362 loss3 :  0.01777667924761772\n",
      "loss4 :  0.0029434203170239925 loss5 :  0.017089366912841797\n",
      "time taken :  0.568371057510376\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005804824642837048\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.00011482238915050402 loss2 :  0.0011363982921466231 loss3 :  0.0003570556582417339\n",
      "loss4 :  0.001620388007722795 loss5 :  0.002575778868049383\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005282497499138117\n",
      "lossl :  0.0 loss1 :  0.00022392273240257055 loss2 :  0.0018958091968670487 loss3 :  0.001225376152433455\n",
      "loss4 :  0.0016102790832519531 loss5 :  0.00032711029052734375\n",
      "Iteration :  7  /  7\n",
      "loss :  0.027769185602664948\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.0038450241554528475 loss2 :  0.001310157822445035 loss3 :  0.0039772032760083675\n",
      "loss4 :  0.0027734756004065275 loss5 :  0.015862464904785156\n",
      "time taken :  0.17853879928588867\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 54/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010428190231323242\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0006269455188885331 loss2 :  0.006788015365600586 loss3 :  0.000704860663972795\n",
      "loss4 :  0.0007125854608602822 loss5 :  0.0015955924754962325\n",
      "Iteration :  4  /  7\n",
      "loss :  0.6010127067565918\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0071388245560228825 loss2 :  0.48490676283836365 loss3 :  0.07731293141841888\n",
      "loss4 :  0.029257584363222122 loss5 :  0.0023963928688317537\n",
      "Iteration :  7  /  7\n",
      "loss :  0.10495519638061523\n",
      "lossl :  0.0 loss1 :  0.0016901970375329256 loss2 :  0.0033338547218590975 loss3 :  0.0001010894775390625\n",
      "loss4 :  0.00332984933629632 loss5 :  0.0965002030134201\n",
      "time taken :  0.5717804431915283\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.003925228025764227\n",
      "lossl :  0.0 loss1 :  0.0005018234369345009 loss2 :  0.00044269562931731343 loss3 :  0.00042438507080078125\n",
      "loss4 :  0.0023755072616040707 loss5 :  0.0001808166562113911\n",
      "Iteration :  4  /  7\n",
      "loss :  0.10855283588171005\n",
      "lossl :  0.0 loss1 :  0.002695274306461215 loss2 :  0.0014367103576660156 loss3 :  0.08281316608190536\n",
      "loss4 :  0.021401310339570045 loss5 :  0.0002063751162495464\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01317610777914524\n",
      "lossl :  1.23977656585339e-06 loss1 :  0.001384735107421875 loss2 :  0.001247358275577426 loss3 :  0.0017941475380212069\n",
      "loss4 :  0.00047597885713912547 loss5 :  0.008272647857666016\n",
      "time taken :  0.1782083511352539\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 55/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.006318855565041304\n",
      "lossl :  0.0 loss1 :  0.00016889572725631297 loss2 :  0.002412128495052457 loss3 :  0.001528835273347795\n",
      "loss4 :  5.8841706049861386e-05 loss5 :  0.0021501542069017887\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2821255922317505\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.02469806745648384 loss2 :  0.14930228888988495 loss3 :  0.03463189676403999\n",
      "loss4 :  0.05634868144989014 loss5 :  0.017144393175840378\n",
      "Iteration :  7  /  7\n",
      "loss :  0.011944914236664772\n",
      "lossl :  0.0 loss1 :  0.0007241248968057334 loss2 :  0.001825523329898715 loss3 :  0.008814859203994274\n",
      "loss4 :  6.237030174816027e-05 loss5 :  0.0005180359003134072\n",
      "time taken :  0.5726559162139893\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0022791861556470394\n",
      "lossl :  0.0 loss1 :  0.000888824462890625 loss2 :  0.00029115675715729594 loss3 :  0.0007953643798828125\n",
      "loss4 :  0.00010414123244117945 loss5 :  0.00019969939603470266\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005419349297881126\n",
      "lossl :  0.0 loss1 :  0.00010786056373035535 loss2 :  0.00045986176701262593 loss3 :  0.0006769180181436241\n",
      "loss4 :  0.0015245437389239669 loss5 :  0.0026501654647290707\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006196403410285711\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.00016336441331077367 loss2 :  0.0005940437549725175 loss3 :  0.0022443770430982113\n",
      "loss4 :  0.0007379531743936241 loss5 :  0.002456188201904297\n",
      "time taken :  0.17537474632263184\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 56/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.25115957856178284\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.027486372739076614 loss2 :  0.028044700622558594 loss3 :  0.11974094063043594\n",
      "loss4 :  0.01462564431130886 loss5 :  0.061261750757694244\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004363441839814186\n",
      "lossl :  1.9359587895451114e-05 loss1 :  0.00024356841458939016 loss2 :  0.0002743721124716103 loss3 :  0.0002758026239462197\n",
      "loss4 :  0.0020624161697924137 loss5 :  0.0014879226218909025\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009284162893891335\n",
      "lossl :  0.0 loss1 :  9.884834435069934e-05 loss2 :  0.002826786134392023 loss3 :  0.0008570671197958291\n",
      "loss4 :  0.005235481075942516 loss5 :  0.00026597976102493703\n",
      "time taken :  0.5717175006866455\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0013884544605389237\n",
      "lossl :  0.0 loss1 :  0.00024061203293967992 loss2 :  3.871917579090223e-05 loss3 :  0.0006832123035565019\n",
      "loss4 :  0.00022516251192428172 loss5 :  0.00020074844360351562\n",
      "Iteration :  4  /  7\n",
      "loss :  0.12613973021507263\n",
      "lossl :  0.0 loss1 :  0.0026522637344896793 loss2 :  0.0001924514799611643 loss3 :  0.10862398147583008\n",
      "loss4 :  0.012858772650361061 loss5 :  0.0018122673500329256\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005402469541877508\n",
      "lossl :  0.0 loss1 :  0.0005268097156658769 loss2 :  0.0012974739074707031 loss3 :  0.00112323765642941\n",
      "loss4 :  0.0022614479530602694 loss5 :  0.00019350051297806203\n",
      "time taken :  0.17693114280700684\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 57/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.006668281741440296\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.0002655029238667339 loss2 :  8.316039748024195e-05 loss3 :  0.0002799034118652344\n",
      "loss4 :  0.001071929931640625 loss5 :  0.004967308137565851\n",
      "Iteration :  4  /  7\n",
      "loss :  0.04956989362835884\n",
      "lossl :  0.0 loss1 :  8.859634544933215e-05 loss2 :  0.0045073507353663445 loss3 :  0.0015954971313476562\n",
      "loss4 :  0.0009091377141885459 loss5 :  0.04246931150555611\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4636436104774475\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0015889644855633378 loss2 :  0.0435522086918354 loss3 :  0.01953563652932644\n",
      "loss4 :  0.3984759449958801 loss5 :  0.0004906654357910156\n",
      "time taken :  0.5601596832275391\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00819616299122572\n",
      "lossl :  0.0 loss1 :  0.0001909255952341482 loss2 :  0.0009145736694335938 loss3 :  0.002038288163021207\n",
      "loss4 :  0.0042283060029149055 loss5 :  0.0008240699535235763\n",
      "Iteration :  4  /  7\n",
      "loss :  0.7294471859931946\n",
      "lossl :  0.0 loss1 :  0.012417030520737171 loss2 :  0.07387576252222061 loss3 :  0.09606198966503143\n",
      "loss4 :  0.48500996828079224 loss5 :  0.062082480639219284\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01001429557800293\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00017070770263671875 loss2 :  0.0007183075067587197 loss3 :  0.004877996630966663\n",
      "loss4 :  0.0009345054859295487 loss5 :  0.0033125877380371094\n",
      "time taken :  0.16509294509887695\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 58/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1921687126159668\n",
      "lossl :  0.0 loss1 :  8.96453821042087e-06 loss2 :  0.012422561645507812 loss3 :  0.032063961029052734\n",
      "loss4 :  0.00661811837926507 loss5 :  0.14105510711669922\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0669519454240799\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.007804298307746649 loss2 :  0.008542919531464577 loss3 :  0.0006773948553018272\n",
      "loss4 :  0.016363143920898438 loss5 :  0.033563993871212006\n",
      "Iteration :  7  /  7\n",
      "loss :  0.11971182376146317\n",
      "lossl :  0.0 loss1 :  0.014489221386611462 loss2 :  0.0006652831798419356 loss3 :  0.002031993819400668\n",
      "loss4 :  0.008397865109145641 loss5 :  0.09412746131420135\n",
      "time taken :  0.5595748424530029\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00961289368569851\n",
      "lossl :  0.0 loss1 :  0.0006482601165771484 loss2 :  0.00028171538724564016 loss3 :  0.0014236450660973787\n",
      "loss4 :  0.0032640458084642887 loss5 :  0.003995227627456188\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006721449550241232\n",
      "lossl :  0.0 loss1 :  0.0022839070297777653 loss2 :  0.0009348869207315147 loss3 :  0.0022726058959960938\n",
      "loss4 :  0.0009289741283282638 loss5 :  0.0003010749933309853\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1896856129169464\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.003974723629653454 loss2 :  0.0006843566661700606 loss3 :  0.18069323897361755\n",
      "loss4 :  0.003498554229736328 loss5 :  0.000834560371004045\n",
      "time taken :  0.16838812828063965\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 59/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.12508101761341095\n",
      "lossl :  7.152557373046875e-06 loss1 :  0.013851070776581764 loss2 :  0.0025548457633703947 loss3 :  0.024256229400634766\n",
      "loss4 :  0.011085080914199352 loss5 :  0.07332663238048553\n",
      "Iteration :  4  /  7\n",
      "loss :  0.04743156582117081\n",
      "lossl :  3.24249276673072e-06 loss1 :  0.0002246856747660786 loss2 :  0.008138751611113548 loss3 :  0.013735485263168812\n",
      "loss4 :  0.001607608748599887 loss5 :  0.023721789941191673\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01690821722149849\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.005390834994614124 loss2 :  0.005223703570663929 loss3 :  0.0014655112754553556\n",
      "loss4 :  0.0036509514320641756 loss5 :  0.0011763572692871094\n",
      "time taken :  0.5600051879882812\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07066138088703156\n",
      "lossl :  0.0 loss1 :  0.01348409615457058 loss2 :  0.0038623095024377108 loss3 :  0.00821905117481947\n",
      "loss4 :  0.004720592405647039 loss5 :  0.04037532955408096\n",
      "Iteration :  4  /  7\n",
      "loss :  0.06614474952220917\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.00019979476928710938 loss2 :  0.0009706497075967491 loss3 :  0.005056953523308039\n",
      "loss4 :  0.05441112443804741 loss5 :  0.005505370907485485\n",
      "Iteration :  7  /  7\n",
      "loss :  0.8783547878265381\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.10591168701648712 loss2 :  0.0006021499866619706 loss3 :  0.08055286109447479\n",
      "loss4 :  0.38019052147865295 loss5 :  0.31109753251075745\n",
      "time taken :  0.16698575019836426\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 60/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.2396230399608612\n",
      "lossl :  0.0 loss1 :  0.014859771355986595 loss2 :  0.0014810562133789062 loss3 :  0.03637118265032768\n",
      "loss4 :  0.016121482476592064 loss5 :  0.17078953981399536\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010598374530673027\n",
      "lossl :  9.536743306171047e-08 loss1 :  2.6798248654813506e-05 loss2 :  0.0038891793228685856 loss3 :  0.001264381455257535\n",
      "loss4 :  0.001016330672428012 loss5 :  0.004401588346809149\n",
      "Iteration :  7  /  7\n",
      "loss :  0.015123939141631126\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.006561183836311102 loss2 :  0.0015905380714684725 loss3 :  0.0007377624278888106\n",
      "loss4 :  0.005974292755126953 loss5 :  0.0002597808779682964\n",
      "time taken :  0.5565814971923828\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.0251238346099854\n",
      "lossl :  0.0 loss1 :  0.11747145652770996 loss2 :  0.0002929687616415322 loss3 :  0.09145407378673553\n",
      "loss4 :  0.4608604311943054 loss5 :  0.3550449311733246\n",
      "Iteration :  4  /  7\n",
      "loss :  0.06664681434631348\n",
      "lossl :  0.0 loss1 :  0.01011114101856947 loss2 :  0.004771900363266468 loss3 :  0.0070588113740086555\n",
      "loss4 :  0.005167961120605469 loss5 :  0.03953700140118599\n",
      "Iteration :  7  /  7\n",
      "loss :  0.22810997068881989\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.004447841551154852 loss2 :  0.0014758110046386719 loss3 :  0.21348586678504944\n",
      "loss4 :  0.003972148988395929 loss5 :  0.0047279358841478825\n",
      "time taken :  0.16724109649658203\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 61/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01272497233003378\n",
      "lossl :  0.0 loss1 :  0.0002830505254678428 loss2 :  0.009190845303237438 loss3 :  0.0009204864618368447\n",
      "loss4 :  0.0011104583973065019 loss5 :  0.0012201309436932206\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00190143589861691\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0012014389503747225 loss2 :  0.00017032623873092234 loss3 :  0.00018959045701194555\n",
      "loss4 :  0.00025234222994185984 loss5 :  8.735656592762098e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.022927476093173027\n",
      "lossl :  0.0 loss1 :  0.006980037782341242 loss2 :  0.0015045165782794356 loss3 :  0.0019652366172522306\n",
      "loss4 :  0.011869335547089577 loss5 :  0.0006083488697186112\n",
      "time taken :  0.560859203338623\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.7942070364952087\n",
      "lossl :  9.536743306171047e-08 loss1 :  9.460448927711695e-05 loss2 :  0.10129117965698242 loss3 :  0.13940314948558807\n",
      "loss4 :  0.5531284213066101 loss5 :  0.0002896309015341103\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005972814746201038\n",
      "lossl :  0.0 loss1 :  0.0020559788681566715 loss2 :  0.0004810333193745464 loss3 :  0.0023433684837073088\n",
      "loss4 :  0.0007719993591308594 loss5 :  0.0003204345703125\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007870197296142578\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.0004307747003622353 loss2 :  0.0009974479908123612 loss3 :  0.002187061356380582\n",
      "loss4 :  0.001146030379459262 loss5 :  0.0031078339088708162\n",
      "time taken :  0.16550326347351074\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 62/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.009799384512007236\n",
      "lossl :  3.337860107421875e-06 loss1 :  4.7206878662109375e-05 loss2 :  0.008783245459198952 loss3 :  0.00012407303438521922\n",
      "loss4 :  0.00020532608323264867 loss5 :  0.000636196113191545\n",
      "Iteration :  4  /  7\n",
      "loss :  0.4527406692504883\n",
      "lossl :  0.0 loss1 :  0.002063751220703125 loss2 :  0.0022262572310864925 loss3 :  0.0002309799165232107\n",
      "loss4 :  0.3787570893764496 loss5 :  0.06946258246898651\n",
      "Iteration :  7  /  7\n",
      "loss :  0.17686091363430023\n",
      "lossl :  0.0 loss1 :  0.11925502121448517 loss2 :  0.018053913488984108 loss3 :  0.010975122451782227\n",
      "loss4 :  0.004329967312514782 loss5 :  0.024246882647275925\n",
      "time taken :  0.566727876663208\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07529189437627792\n",
      "lossl :  7.629394644936838e-07 loss1 :  0.01342925988137722 loss2 :  0.005353760905563831 loss3 :  0.009878253564238548\n",
      "loss4 :  0.0004493713495321572 loss5 :  0.04618048667907715\n",
      "Iteration :  4  /  7\n",
      "loss :  0.3338136076927185\n",
      "lossl :  0.0 loss1 :  0.004641389939934015 loss2 :  0.0004848480166401714 loss3 :  0.32544708251953125\n",
      "loss4 :  0.0022602081298828125 loss5 :  0.0009800910484045744\n",
      "Iteration :  7  /  7\n",
      "loss :  0.008800983428955078\n",
      "lossl :  0.0 loss1 :  0.00038928986759856343 loss2 :  0.0010542869567871094 loss3 :  0.00299835205078125\n",
      "loss4 :  0.003345394041389227 loss5 :  0.0010136604541912675\n",
      "time taken :  0.17640185356140137\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 63/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06290650367736816\n",
      "lossl :  0.0 loss1 :  0.045420218259096146 loss2 :  0.0006914138793945312 loss3 :  0.01408376730978489\n",
      "loss4 :  0.0011641501914709806 loss5 :  0.0015469550853595138\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00583381624892354\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0029077529907226562 loss2 :  0.002609634306281805 loss3 :  9.17434663278982e-05\n",
      "loss4 :  0.00020294189744163305 loss5 :  2.136230432370212e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.012361192144453526\n",
      "lossl :  0.0 loss1 :  0.006731605622917414 loss2 :  0.0002554893435444683 loss3 :  0.0018372535705566406\n",
      "loss4 :  0.00025663376436568797 loss5 :  0.003280210541561246\n",
      "time taken :  0.5738437175750732\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.14123296737670898\n",
      "lossl :  0.0 loss1 :  0.002680015517398715 loss2 :  0.00036220549372956157 loss3 :  0.1359119415283203\n",
      "loss4 :  0.001550388289615512 loss5 :  0.0007284164312295616\n",
      "Iteration :  4  /  7\n",
      "loss :  0.7004933953285217\n",
      "lossl :  0.0 loss1 :  0.00028209685115143657 loss2 :  0.109775111079216 loss3 :  0.09404172748327255\n",
      "loss4 :  0.4962746202945709 loss5 :  0.00011987685866188258\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009022903628647327\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0023669241927564144 loss2 :  0.0007595062488690019 loss3 :  0.003370094345882535\n",
      "loss4 :  0.0015539169544354081 loss5 :  0.000971889472566545\n",
      "time taken :  0.17624258995056152\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 64/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0050439839251339436\n",
      "lossl :  0.0 loss1 :  3.0422210329561494e-05 loss2 :  0.000148773193359375 loss3 :  0.0002758026239462197\n",
      "loss4 :  0.004472923465073109 loss5 :  0.00011606216139625758\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003161811735481024\n",
      "lossl :  0.0 loss1 :  1.5258789289873675e-06 loss2 :  8.0108642578125e-05 loss3 :  0.0015460491413250566\n",
      "loss4 :  0.0007240772247314453 loss5 :  0.0008100509876385331\n",
      "Iteration :  7  /  7\n",
      "loss :  0.029749251902103424\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.00458140391856432 loss2 :  0.004826164338737726 loss3 :  0.0005115509266033769\n",
      "loss4 :  0.0020257949363440275 loss5 :  0.0178038589656353\n",
      "time taken :  0.5786547660827637\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0006651878356933594\n",
      "lossl :  0.0 loss1 :  0.00022916794114280492 loss2 :  0.00010261535499012098 loss3 :  0.00011501312110340223\n",
      "loss4 :  0.0001791000395314768 loss5 :  3.929138256353326e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0011234283447265625\n",
      "lossl :  0.0 loss1 :  5.2165985835017636e-05 loss2 :  0.00017747879610396922 loss3 :  0.0004295349062886089\n",
      "loss4 :  0.0002898216189350933 loss5 :  0.00017442702664993703\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00090112688485533\n",
      "lossl :  0.0 loss1 :  2.002716064453125e-05 loss2 :  8.478164818370715e-05 loss3 :  0.0003371238708496094\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss4 :  0.00013999939255882055 loss5 :  0.00031919480534270406\n",
      "time taken :  0.18164515495300293\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 65/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07004842162132263\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0024917603004723787 loss2 :  0.031273555010557175 loss3 :  0.018372725695371628\n",
      "loss4 :  0.017595957964658737 loss5 :  0.00031404494075104594\n",
      "Iteration :  4  /  7\n",
      "loss :  0.030690716579556465\n",
      "lossl :  0.0 loss1 :  3.719329833984375e-05 loss2 :  0.0258928295224905 loss3 :  0.00025644301786087453\n",
      "loss4 :  0.004122591111809015 loss5 :  0.00038166047306731343\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0009860038990154862\n",
      "lossl :  0.0 loss1 :  0.0001108169526560232 loss2 :  1.506805438111769e-05 loss3 :  0.0007281303405761719\n",
      "loss4 :  7.114410254871473e-05 loss5 :  6.084442065912299e-05\n",
      "time taken :  0.569115400314331\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002803707029670477\n",
      "lossl :  0.0 loss1 :  0.0001125335693359375 loss2 :  0.0004002571222372353 loss3 :  0.0006918907165527344\n",
      "loss4 :  0.0011182784801349044 loss5 :  0.0004807472287211567\n",
      "Iteration :  4  /  7\n",
      "loss :  0.02909565158188343\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0021241188514977694 loss2 :  0.0006185531383380294 loss3 :  0.021558571606874466\n",
      "loss4 :  0.002523231552913785 loss5 :  0.002270984696224332\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5495262145996094\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.07118158042430878 loss2 :  0.001227521919645369 loss3 :  0.015775013715028763\n",
      "loss4 :  0.2623271346092224 loss5 :  0.1990145742893219\n",
      "time taken :  0.17793583869934082\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 66/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.014864826574921608\n",
      "lossl :  3.0994415283203125e-05 loss1 :  0.0007605552673339844 loss2 :  0.006146716885268688 loss3 :  0.0010272025829181075\n",
      "loss4 :  0.004143810365349054 loss5 :  0.0027555464766919613\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2074669748544693\n",
      "lossl :  0.0 loss1 :  0.0003468513605184853 loss2 :  0.1904647797346115 loss3 :  0.01103362999856472\n",
      "loss4 :  0.002277851104736328 loss5 :  0.003343868302181363\n",
      "Iteration :  7  /  7\n",
      "loss :  0.010761832818388939\n",
      "lossl :  0.0 loss1 :  0.0001354217529296875 loss2 :  7.543563697254285e-05 loss3 :  0.0036673545837402344\n",
      "loss4 :  0.00040311814518645406 loss5 :  0.006480502896010876\n",
      "time taken :  0.5687470436096191\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0018685341347008944\n",
      "lossl :  0.0 loss1 :  9.317397780250758e-05 loss2 :  0.0005109786870889366 loss3 :  0.0004067420959472656\n",
      "loss4 :  0.0005136489635333419 loss5 :  0.00034399033756926656\n",
      "Iteration :  4  /  7\n",
      "loss :  0.017251968383789062\n",
      "lossl :  0.0 loss1 :  0.0017779350746423006 loss2 :  0.0001371383696096018 loss3 :  0.013234901241958141\n",
      "loss4 :  0.0020085335709154606 loss5 :  9.34600830078125e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0029875754844397306\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.000453948974609375 loss2 :  0.00031938552274368703 loss3 :  0.0013844489585608244\n",
      "loss4 :  0.00034008026705123484 loss5 :  0.0004895210149697959\n",
      "time taken :  0.1786022186279297\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 67/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.016951370984315872\n",
      "lossl :  9.250640687241685e-06 loss1 :  0.003627777099609375 loss2 :  0.0033813477493822575 loss3 :  0.0005126952892169356\n",
      "loss4 :  0.0049034119583666325 loss5 :  0.004516887478530407\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007114076521247625\n",
      "lossl :  0.0 loss1 :  0.001712846802547574 loss2 :  0.0013288498157635331 loss3 :  0.003672981169074774\n",
      "loss4 :  0.00031986235990189016 loss5 :  7.953643944347277e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00838084239512682\n",
      "lossl :  2.86102294921875e-06 loss1 :  0.0001621246337890625 loss2 :  0.0006905555492267013 loss3 :  0.004703951068222523\n",
      "loss4 :  0.0007793426630087197 loss5 :  0.0020420073997229338\n",
      "time taken :  0.5669872760772705\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.009078025817871094\n",
      "lossl :  0.0 loss1 :  0.00013618469529319555 loss2 :  0.00023899078951217234 loss3 :  0.0020399093627929688\n",
      "loss4 :  0.005120372865349054 loss5 :  0.0015425682067871094\n",
      "Iteration :  4  /  7\n",
      "loss :  0.4404332637786865\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0003628730773925781 loss2 :  0.04227862507104874 loss3 :  0.08057765662670135\n",
      "loss4 :  0.31706055998802185 loss5 :  0.00015335083298850805\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0008232116815634072\n",
      "lossl :  0.0 loss1 :  2.040863000729587e-05 loss2 :  0.0001407623349223286 loss3 :  0.00014200209989212453\n",
      "loss4 :  0.00023565292940475047 loss5 :  0.00028438569279387593\n",
      "time taken :  0.16638541221618652\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 68/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.26086312532424927\n",
      "lossl :  6.48498553346144e-06 loss1 :  0.0038434029556810856 loss2 :  0.009008216671645641 loss3 :  0.23644134402275085\n",
      "loss4 :  0.006224346347153187 loss5 :  0.005339336581528187\n",
      "Iteration :  4  /  7\n",
      "loss :  0.039473194628953934\n",
      "lossl :  1.7642974853515625e-05 loss1 :  0.0022852898109704256 loss2 :  0.013526964001357555 loss3 :  0.022014807909727097\n",
      "loss4 :  0.0005220413440838456 loss5 :  0.0011064528953284025\n",
      "Iteration :  7  /  7\n",
      "loss :  0.014329052530229092\n",
      "lossl :  0.0 loss1 :  6.09397902735509e-05 loss2 :  0.007161140441894531 loss3 :  0.0012044906616210938\n",
      "loss4 :  0.000644683837890625 loss5 :  0.005257797427475452\n",
      "time taken :  0.5594995021820068\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0006889342912472785\n",
      "lossl :  0.0 loss1 :  0.00011920928955078125 loss2 :  9.593963477527723e-05 loss3 :  0.0002521514834370464\n",
      "loss4 :  0.0001808166562113911 loss5 :  4.081726001459174e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.014502143487334251\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.00018005371384788305 loss2 :  0.00043315888615325093 loss3 :  0.0029349327087402344\n",
      "loss4 :  0.00828628521412611 loss5 :  0.002667331602424383\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4502217769622803\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0398712158203125 loss2 :  0.0004904746892862022 loss3 :  0.012279796414077282\n",
      "loss4 :  0.2215532809495926 loss5 :  0.1760268211364746\n",
      "time taken :  0.16617393493652344\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 69/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0521126314997673\n",
      "lossl :  0.0 loss1 :  0.0006347656017169356 loss2 :  0.007066965103149414 loss3 :  0.031536199152469635\n",
      "loss4 :  0.0030366897117346525 loss5 :  0.009838009253144264\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00341205601580441\n",
      "lossl :  2.8610230629055877e-07 loss1 :  4.873275611316785e-05 loss2 :  0.0017230033408850431 loss3 :  0.0006196022150106728\n",
      "loss4 :  0.0009477615240029991 loss5 :  7.26699799997732e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004577255342155695\n",
      "lossl :  0.0 loss1 :  3.814697265625e-06 loss2 :  0.00010013580322265625 loss3 :  1.163482647825731e-05\n",
      "loss4 :  0.004335212521255016 loss5 :  0.00012645722017623484\n",
      "time taken :  0.5584926605224609\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.37615618109703064\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.031864263117313385 loss2 :  0.0006990432739257812 loss3 :  0.012741279788315296\n",
      "loss4 :  0.1692560613155365 loss5 :  0.1615946739912033\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004905796144157648\n",
      "lossl :  0.0 loss1 :  0.00019350051297806203 loss2 :  0.0006521224859170616 loss3 :  0.0011080742115154862\n",
      "loss4 :  0.0023213387466967106 loss5 :  0.0006307602161541581\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00045986176701262593\n",
      "lossl :  0.0 loss1 :  4.854202416026965e-05 loss2 :  5.3119660151423886e-05 loss3 :  9.822845458984375e-05\n",
      "loss4 :  0.00020484924607444555 loss5 :  5.512237476068549e-05\n",
      "time taken :  0.16680908203125\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 70/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.0014245986239984632\n",
      "lossl :  0.0 loss1 :  0.00042448044405318797 loss2 :  0.00011177062697242945 loss3 :  5.111694190418348e-05\n",
      "loss4 :  0.0007529258728027344 loss5 :  8.430481102550402e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.055332086980342865\n",
      "lossl :  0.0 loss1 :  0.001321506453678012 loss2 :  0.019735049456357956 loss3 :  0.017871856689453125\n",
      "loss4 :  0.009703731164336205 loss5 :  0.006699943449348211\n",
      "Iteration :  7  /  7\n",
      "loss :  0.02248668670654297\n",
      "lossl :  0.0 loss1 :  0.0022471428383141756 loss2 :  0.01943521574139595 loss3 :  0.00028057099552825093\n",
      "loss4 :  0.00040540695772506297 loss5 :  0.0001183509812108241\n",
      "time taken :  0.560438871383667\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.7944373488426208\n",
      "lossl :  0.0 loss1 :  8.525848534191027e-05 loss2 :  0.116075798869133 loss3 :  0.17494049668312073\n",
      "loss4 :  0.5022468566894531 loss5 :  0.001088905381038785\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0007522582891397178\n",
      "lossl :  0.0 loss1 :  6.370544724632055e-05 loss2 :  0.00012998581223655492 loss3 :  0.00022983551025390625\n",
      "loss4 :  0.0002537727414164692 loss5 :  7.495879981433973e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010571479797363281\n",
      "lossl :  0.0 loss1 :  9.72747784544481e-06 loss2 :  0.0003811836359091103 loss3 :  0.0003188133123330772\n",
      "loss4 :  0.00022287368483375758 loss5 :  0.00012454987154342234\n",
      "time taken :  0.16589617729187012\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 71/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04144792631268501\n",
      "lossl :  0.0 loss1 :  0.0009327888255938888 loss2 :  0.002276134444400668 loss3 :  0.006592369172722101\n",
      "loss4 :  0.0018982887268066406 loss5 :  0.02974834479391575\n",
      "Iteration :  4  /  7\n",
      "loss :  0.5426273345947266\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.000637054443359375 loss2 :  0.02689390257000923 loss3 :  0.020913314074277878\n",
      "loss4 :  0.37011292576789856 loss5 :  0.12406966835260391\n",
      "Iteration :  7  /  7\n",
      "loss :  0.015279103070497513\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0036623000632971525 loss2 :  0.00206165318377316 loss3 :  0.007705974392592907\n",
      "loss4 :  0.0016893387073650956 loss5 :  0.00015964507474564016\n",
      "time taken :  0.5624065399169922\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.001628017402254045\n",
      "lossl :  0.0 loss1 :  8.649825758766383e-05 loss2 :  0.0004103660467080772 loss3 :  0.0005055427318438888\n",
      "loss4 :  0.0004363059997558594 loss5 :  0.00018930435180664062\n",
      "Iteration :  4  /  7\n",
      "loss :  0.7968597412109375\n",
      "lossl :  0.0 loss1 :  0.019997309893369675 loss2 :  0.05625209957361221 loss3 :  0.12284193187952042\n",
      "loss4 :  0.5082558989524841 loss5 :  0.08951244503259659\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006482553202658892\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0007282256847247481 loss2 :  0.00031766892061568797 loss3 :  0.0050623416900634766\n",
      "loss4 :  0.00017871856107376516 loss5 :  0.00019540786161087453\n",
      "time taken :  0.16507220268249512\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 72/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.16388797760009766\n",
      "lossl :  6.29425039733178e-06 loss1 :  0.00038614272489212453 loss2 :  0.010151100344955921 loss3 :  0.030033206567168236\n",
      "loss4 :  0.11740932613611221 loss5 :  0.005901908967643976\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0048656463623046875\n",
      "lossl :  1.5258789289873675e-06 loss1 :  0.0001293182431254536 loss2 :  0.0018551826942712069 loss3 :  0.00012426376633811742\n",
      "loss4 :  0.0023118972312659025 loss5 :  0.00044345855712890625\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00464177131652832\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.00045075415982864797 loss2 :  0.0011837959755212069 loss3 :  0.0012989997630938888\n",
      "loss4 :  0.00060949323233217 loss5 :  0.0010976791381835938\n",
      "time taken :  0.5587148666381836\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002330589108169079\n",
      "lossl :  0.0 loss1 :  5.359649730962701e-05 loss2 :  0.0006415367242880166 loss3 :  0.0004871368291787803\n",
      "loss4 :  0.0008159637218341231 loss5 :  0.0003323554992675781\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2856292426586151\n",
      "lossl :  0.0 loss1 :  8.239746239269152e-05 loss2 :  0.018400192260742188 loss3 :  0.03787956386804581\n",
      "loss4 :  0.2291455715894699 loss5 :  0.00012149810936534777\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00271854386664927\n",
      "lossl :  0.0 loss1 :  0.0017168044578284025 loss2 :  0.00010643005225574598 loss3 :  1.487731969973538e-05\n",
      "loss4 :  0.0005058288807049394 loss5 :  0.0003746032598428428\n",
      "time taken :  0.16467499732971191\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 73/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.40837234258651733\n",
      "lossl :  0.0 loss1 :  0.05679967254400253 loss2 :  0.14315319061279297 loss3 :  0.15223093330860138\n",
      "loss4 :  0.05411076545715332 loss5 :  0.002077770186588168\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010696864686906338\n",
      "lossl :  0.0 loss1 :  0.0014386177062988281 loss2 :  0.0009263753890991211 loss3 :  0.007989788427948952\n",
      "loss4 :  0.000110626220703125 loss5 :  0.00023145675368141383\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01943054236471653\n",
      "lossl :  0.0 loss1 :  0.00019502639770507812 loss2 :  0.0008987426990643144 loss3 :  0.009770775213837624\n",
      "loss4 :  0.006920814514160156 loss5 :  0.0016451835399493575\n",
      "time taken :  0.5644164085388184\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00031299592228606343\n",
      "lossl :  0.0 loss1 :  0.000110626220703125 loss2 :  2.040863000729587e-05 loss3 :  7.658005051780492e-05\n",
      "loss4 :  8.487701416015625e-05 loss5 :  2.0503997802734375e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0010137557983398438\n",
      "lossl :  0.0 loss1 :  8.56399565236643e-05 loss2 :  8.878707740223035e-05 loss3 :  0.00010833740088855848\n",
      "loss4 :  0.0005001068348065019 loss5 :  0.00023088455782271922\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006921959109604359\n",
      "lossl :  0.0 loss1 :  0.001989173935726285 loss2 :  0.0013583183754235506 loss3 :  0.0006046295166015625\n",
      "loss4 :  0.0010652542114257812 loss5 :  0.0019045829540118575\n",
      "time taken :  0.18037652969360352\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 74/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.12822088599205017\n",
      "lossl :  2.288818359375e-05 loss1 :  0.00315265660174191 loss2 :  0.005482387728989124 loss3 :  0.0035026550758630037\n",
      "loss4 :  0.06952209770679474 loss5 :  0.04653821140527725\n",
      "Iteration :  4  /  7\n",
      "loss :  0.08782579749822617\n",
      "lossl :  0.0 loss1 :  0.034241270273923874 loss2 :  0.010097503662109375 loss3 :  0.0013362884055823088\n",
      "loss4 :  0.04054522514343262 loss5 :  0.0016055107116699219\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08985519409179688\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.022985171526670456 loss2 :  0.004708719439804554 loss3 :  0.05792517587542534\n",
      "loss4 :  0.0032248497009277344 loss5 :  0.0010110854636877775\n",
      "time taken :  0.5683636665344238\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002982235047966242\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0002942085266113281 loss2 :  0.0005117416149005294 loss3 :  0.0002972602960653603\n",
      "loss4 :  0.0003017425478901714 loss5 :  0.001577091170474887\n",
      "Iteration :  4  /  7\n",
      "loss :  0.002899360377341509\n",
      "lossl :  0.0 loss1 :  0.001400852226652205 loss2 :  0.00016536712064407766 loss3 :  0.00086126325186342\n",
      "loss4 :  6.570816185558215e-05 loss5 :  0.0004061698855366558\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010348319774493575\n",
      "lossl :  0.0 loss1 :  0.00011940002150367945 loss2 :  7.696151442360133e-05 loss3 :  0.00017518996901344508\n",
      "loss4 :  0.00043048858060501516 loss5 :  0.00023279190645553172\n",
      "time taken :  0.17987751960754395\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 75/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.011974764056503773\n",
      "lossl :  0.0 loss1 :  0.0008565426105633378 loss2 :  0.0006666183471679688 loss3 :  0.0001107215866795741\n",
      "loss4 :  0.007693481631577015 loss5 :  0.00264739990234375\n",
      "Iteration :  4  /  7\n",
      "loss :  0.029462339356541634\n",
      "lossl :  0.0 loss1 :  0.02525930479168892 loss2 :  0.001386928604915738 loss3 :  0.0022673606872558594\n",
      "loss4 :  0.00018739700317382812 loss5 :  0.00036134719266556203\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007113838102668524\n",
      "lossl :  0.0 loss1 :  0.0011684417258948088 loss2 :  0.0003455161931924522 loss3 :  0.0016643523704260588\n",
      "loss4 :  0.0008737564203329384 loss5 :  0.0030617713928222656\n",
      "time taken :  0.6088175773620605\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0032974244095385075\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0002787589910440147 loss2 :  0.00018320084200240672 loss3 :  0.0024345398414880037\n",
      "loss4 :  0.00018005371384788305 loss5 :  0.00022058487229514867\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0014202117454260588\n",
      "lossl :  0.0 loss1 :  0.00018959045701194555 loss2 :  0.0003895759582519531 loss3 :  0.00030422210693359375\n",
      "loss4 :  0.00041904448880814016 loss5 :  0.00011777877807617188\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003479385282844305\n",
      "lossl :  0.0 loss1 :  0.0015386581653729081 loss2 :  0.00021114348783157766 loss3 :  0.0010038375621661544\n",
      "loss4 :  0.00016498565673828125 loss5 :  0.000560760498046875\n",
      "time taken :  0.17831969261169434\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 76/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005538463592529297\n",
      "lossl :  0.0 loss1 :  0.0001203536958200857 loss2 :  0.00023107528977561742 loss3 :  0.0012237548362463713\n",
      "loss4 :  0.00030918122502043843 loss5 :  0.003654098603874445\n",
      "Iteration :  4  /  7\n",
      "loss :  0.08964748680591583\n",
      "lossl :  0.0 loss1 :  0.05586662143468857 loss2 :  0.005681037902832031 loss3 :  0.024112319573760033\n",
      "loss4 :  0.001964664552360773 loss5 :  0.0020228386856615543\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004666900262236595\n",
      "lossl :  0.0 loss1 :  2.28881845032447e-06 loss2 :  0.003848552703857422 loss3 :  0.00012426376633811742\n",
      "loss4 :  6.961822509765625e-05 loss5 :  0.0006221771473065019\n",
      "time taken :  0.5704848766326904\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004538059234619141\n",
      "lossl :  4.76837158203125e-07 loss1 :  8.068084571277723e-05 loss2 :  0.0011807441478595138 loss3 :  0.0006964683416299522\n",
      "loss4 :  0.00039415358332917094 loss5 :  0.0021855353843420744\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0008587836637161672\n",
      "lossl :  0.0 loss1 :  0.00011119842383777723 loss2 :  0.00015478134446311742 loss3 :  0.00031728745670989156\n",
      "loss4 :  0.0001145362839451991 loss5 :  0.00016098022751975805\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00675201416015625\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.002434635069221258 loss2 :  0.0003486633358988911 loss3 :  0.0031405449844896793\n",
      "loss4 :  0.00011920928955078125 loss5 :  0.0007087707635946572\n",
      "time taken :  0.17750024795532227\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 77/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.018427563831210136\n",
      "lossl :  0.0 loss1 :  0.00862665195018053 loss2 :  3.62396240234375e-05 loss3 :  0.0003190040588378906\n",
      "loss4 :  0.009100628085434437 loss5 :  0.00034503935603424907\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01567249372601509\n",
      "lossl :  0.0 loss1 :  0.0014575958484783769 loss2 :  0.008891868405044079 loss3 :  0.00044879913912154734\n",
      "loss4 :  0.0004059791681356728 loss5 :  0.004468250088393688\n",
      "Iteration :  7  /  7\n",
      "loss :  0.06913666427135468\n",
      "lossl :  1.9073486328125e-06 loss1 :  0.0023852349258959293 loss2 :  0.0029706000350415707 loss3 :  0.00993490219116211\n",
      "loss4 :  0.037340499460697174 loss5 :  0.016503524035215378\n",
      "time taken :  0.5687265396118164\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.006597280502319336\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0003807067987509072 loss2 :  0.0005794524913653731 loss3 :  0.004019403364509344\n",
      "loss4 :  0.0009837150573730469 loss5 :  0.0006338119274005294\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0016351699596270919\n",
      "lossl :  0.0 loss1 :  0.00012636184692382812 loss2 :  0.0003204345703125 loss3 :  0.0005266189691610634\n",
      "loss4 :  0.00043430327787064016 loss5 :  0.00022745132446289062\n",
      "Iteration :  7  /  7\n",
      "loss :  0.13805818557739258\n",
      "lossl :  0.0 loss1 :  0.01695241965353489 loss2 :  3.967285010730848e-05 loss3 :  0.022974491119384766\n",
      "loss4 :  0.04613933712244034 loss5 :  0.051952265202999115\n",
      "time taken :  0.17711353302001953\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 78/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.029156208038330078\n",
      "lossl :  0.0 loss1 :  0.0008441925165243447 loss2 :  0.017611313611268997 loss3 :  0.009960746392607689\n",
      "loss4 :  0.00030460357083939016 loss5 :  0.0004353523254394531\n",
      "Iteration :  4  /  7\n",
      "loss :  0.017607688903808594\n",
      "lossl :  0.0 loss1 :  0.0018245696555823088 loss2 :  0.0005556106334552169 loss3 :  0.007316875271499157\n",
      "loss4 :  0.007487011142075062 loss5 :  0.00042362214298918843\n",
      "Iteration :  7  /  7\n",
      "loss :  0.04140424728393555\n",
      "lossl :  3.919601294910535e-05 loss1 :  0.0017972945934161544 loss2 :  0.005109024234116077 loss3 :  0.01395883597433567\n",
      "loss4 :  0.014840793795883656 loss5 :  0.0056591033935546875\n",
      "time taken :  0.5668172836303711\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0021413802169263363\n",
      "lossl :  0.0 loss1 :  4.77790817967616e-05 loss2 :  0.00014829635620117188 loss3 :  0.00015172958956100047\n",
      "loss4 :  0.0009570121765136719 loss5 :  0.0008365631219930947\n",
      "Iteration :  4  /  7\n",
      "loss :  0.17997002601623535\n",
      "lossl :  0.0 loss1 :  0.03518981859087944 loss2 :  0.002031564712524414 loss3 :  0.03136081621050835\n",
      "loss4 :  0.046253204345703125 loss5 :  0.06513462215662003\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005560636520385742\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0004288673517294228 loss2 :  0.0005819320795126259 loss3 :  0.003480672836303711\n",
      "loss4 :  0.000826930976472795 loss5 :  0.0002420425444142893\n",
      "time taken :  0.16655206680297852\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 79/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005855703726410866\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0004460334894247353 loss2 :  0.0019750117789953947 loss3 :  0.0002082824648823589\n",
      "loss4 :  0.0015990256797522306 loss5 :  0.001627159072086215\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01668081246316433\n",
      "lossl :  0.0 loss1 :  0.00119867327157408 loss2 :  0.00397148123010993 loss3 :  0.0002875328063964844\n",
      "loss4 :  0.010376644320786 loss5 :  0.0008464812999591231\n",
      "Iteration :  7  /  7\n",
      "loss :  0.044029708951711655\n",
      "lossl :  0.0 loss1 :  4.043579247081652e-05 loss2 :  0.0009372711065225303 loss3 :  0.04298868030309677\n",
      "loss4 :  3.166198803228326e-05 loss5 :  3.166198803228326e-05\n",
      "time taken :  0.5826773643493652\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0020884512923657894\n",
      "lossl :  0.0 loss1 :  0.00016927719116210938 loss2 :  0.0003073692205362022 loss3 :  0.0004729270876850933\n",
      "loss4 :  0.0008190154912881553 loss5 :  0.00031986235990189016\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010447883978486061\n",
      "lossl :  0.0 loss1 :  0.00015907287888694555 loss2 :  0.0013962745433673263 loss3 :  0.005751895718276501\n",
      "loss4 :  0.0005233764532022178 loss5 :  0.002617263700813055\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0013234139187261462\n",
      "lossl :  0.0 loss1 :  3.318786548334174e-05 loss2 :  0.0002713203430175781 loss3 :  0.00012845992750953883\n",
      "loss4 :  0.0005729675176553428 loss5 :  0.00031747817411087453\n",
      "time taken :  0.16731595993041992\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 80/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.030196644365787506\n",
      "lossl :  0.0 loss1 :  4.463195728021674e-05 loss2 :  0.028658080846071243 loss3 :  0.0006814002990722656\n",
      "loss4 :  0.0006422043079510331 loss5 :  0.00017032623873092234\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010973453521728516\n",
      "lossl :  0.0 loss1 :  6.933211989235133e-05 loss2 :  0.00030918122502043843 loss3 :  0.00920877419412136\n",
      "loss4 :  0.0007156372303143144 loss5 :  0.00067052838858217\n",
      "Iteration :  7  /  7\n",
      "loss :  0.022020481526851654\n",
      "lossl :  0.0 loss1 :  0.00024127960205078125 loss2 :  0.0036939620040357113 loss3 :  0.000888824462890625\n",
      "loss4 :  0.007583522703498602 loss5 :  0.00961289368569851\n",
      "time taken :  0.5585412979125977\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0037675858475267887\n",
      "lossl :  0.0 loss1 :  8.726119995117188e-05 loss2 :  0.0006241798400878906 loss3 :  0.00019044875807594508\n",
      "loss4 :  0.00016794205293990672 loss5 :  0.0026977539528161287\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0005348206032067537\n",
      "lossl :  0.0 loss1 :  6.30378708592616e-05 loss2 :  0.00016698837862350047 loss3 :  7.05718994140625e-05\n",
      "loss4 :  0.00015172958956100047 loss5 :  8.249282836914062e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.3311409652233124\n",
      "lossl :  0.0 loss1 :  2.6226043701171875e-05 loss2 :  0.0085738655179739 loss3 :  0.04085216671228409\n",
      "loss4 :  0.281610906124115 loss5 :  7.781982276355848e-05\n",
      "time taken :  0.16945505142211914\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 81/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.030842017382383347\n",
      "lossl :  0.0 loss1 :  0.014359807595610619 loss2 :  0.00221595773473382 loss3 :  0.011644840240478516\n",
      "loss4 :  0.001974391983821988 loss5 :  0.0006470203516073525\n",
      "Iteration :  4  /  7\n",
      "loss :  0.027531147003173828\n",
      "lossl :  0.0 loss1 :  0.0012600899208337069 loss2 :  0.004902362823486328 loss3 :  0.004989909939467907\n",
      "loss4 :  0.001116847968660295 loss5 :  0.015261936001479626\n",
      "Iteration :  7  /  7\n",
      "loss :  0.036236099898815155\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0009264945983886719 loss2 :  0.0008358001941815019 loss3 :  0.0314205177128315\n",
      "loss4 :  0.0027153969276696444 loss5 :  0.0003376960812602192\n",
      "time taken :  0.5608046054840088\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00542063731700182\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0005802154773846269 loss2 :  0.00028314589872024953 loss3 :  0.003965520765632391\n",
      "loss4 :  0.00028486252995207906 loss5 :  0.0003067970392294228\n",
      "Iteration :  4  /  7\n",
      "loss :  0.025484371930360794\n",
      "lossl :  0.0 loss1 :  4.673004150390625e-05 loss2 :  5.435943603515625e-05 loss3 :  0.023738384246826172\n",
      "loss4 :  0.0013425827492028475 loss5 :  0.00030231475830078125\n",
      "Iteration :  7  /  7\n",
      "loss :  0.8090183138847351\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0001960754452738911 loss2 :  0.0821874588727951 loss3 :  0.19564342498779297\n",
      "loss4 :  0.5239300727844238 loss5 :  0.007060336880385876\n",
      "time taken :  0.16659140586853027\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 82/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0009066581842489541\n",
      "lossl :  0.0 loss1 :  1.5163421267061494e-05 loss2 :  0.0007762908935546875 loss3 :  1.430511474609375e-05\n",
      "loss4 :  5.054473876953125e-05 loss5 :  5.035400317865424e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07551688700914383\n",
      "lossl :  0.0 loss1 :  0.04208030551671982 loss2 :  0.004009723663330078 loss3 :  0.0059608458541333675\n",
      "loss4 :  0.02159595489501953 loss5 :  0.00187005999032408\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5767027139663696\n",
      "lossl :  0.0 loss1 :  6.732940528308973e-05 loss2 :  0.04261427000164986 loss3 :  0.4449237287044525\n",
      "loss4 :  0.006600809283554554 loss5 :  0.08249659836292267\n",
      "time taken :  0.5598413944244385\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.001475429511629045\n",
      "lossl :  0.0 loss1 :  8.163452002918348e-05 loss2 :  2.5844574338407256e-05 loss3 :  0.0005171775701455772\n",
      "loss4 :  2.8133392333984375e-05 loss5 :  0.0008226394420489669\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0043128011748194695\n",
      "lossl :  0.0 loss1 :  8.726119995117188e-05 loss2 :  0.0004405975341796875 loss3 :  0.000560760498046875\n",
      "loss4 :  0.0017728805541992188 loss5 :  0.00145130162127316\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05130047723650932\n",
      "lossl :  0.0 loss1 :  0.03590879589319229 loss2 :  0.0023894787300378084 loss3 :  0.005518150515854359\n",
      "loss4 :  0.00038042067899368703 loss5 :  0.007103634066879749\n",
      "time taken :  0.16468191146850586\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 83/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06712488830089569\n",
      "lossl :  0.0 loss1 :  0.05071849748492241 loss2 :  0.0019195557106286287 loss3 :  0.013198519125580788\n",
      "loss4 :  0.0005517005920410156 loss5 :  0.0007366180652752519\n",
      "Iteration :  4  /  7\n",
      "loss :  0.4326813817024231\n",
      "lossl :  0.0 loss1 :  0.0026154518127441406 loss2 :  0.0006650924915447831 loss3 :  0.4239357113838196\n",
      "loss4 :  9.002685692394152e-05 loss5 :  0.005375099368393421\n",
      "Iteration :  7  /  7\n",
      "loss :  0.41442549228668213\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0012269973522052169 loss2 :  0.39967307448387146 loss3 :  0.0003869056818075478\n",
      "loss4 :  0.011451482772827148 loss5 :  0.0016864776844158769\n",
      "time taken :  0.5588204860687256\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07633793354034424\n",
      "lossl :  0.0 loss1 :  0.05688977241516113 loss2 :  0.005226778797805309 loss3 :  0.003067112062126398\n",
      "loss4 :  0.0034644126426428556 loss5 :  0.007689857389777899\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03428177908062935\n",
      "lossl :  0.0 loss1 :  0.0002574920654296875 loss2 :  0.001272487686946988 loss3 :  0.029179047793149948\n",
      "loss4 :  0.0008279800531454384 loss5 :  0.0027447701431810856\n",
      "Iteration :  7  /  7\n",
      "loss :  0.42358294129371643\n",
      "lossl :  0.0 loss1 :  1.316070574830519e-05 loss2 :  0.0843203067779541 loss3 :  0.10111560672521591\n",
      "loss4 :  0.23681171238422394 loss5 :  0.0013221740955486894\n",
      "time taken :  0.1737372875213623\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 84/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.5666658878326416\n",
      "lossl :  0.0 loss1 :  0.0015698432689532638 loss2 :  0.34850794076919556 loss3 :  0.2114080935716629\n",
      "loss4 :  0.0031229020096361637 loss5 :  0.0020570755004882812\n",
      "Iteration :  4  /  7\n",
      "loss :  0.023046065121889114\n",
      "lossl :  2.269744800287299e-05 loss1 :  0.0010682105785235763 loss2 :  0.0033190727699548006 loss3 :  0.0026346207596361637\n",
      "loss4 :  0.004667186643928289 loss5 :  0.01133427582681179\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0016329765785485506\n",
      "lossl :  0.0 loss1 :  0.0001049041748046875 loss2 :  6.69479341013357e-05 loss3 :  0.0003265380801167339\n",
      "loss4 :  3.223419116693549e-05 loss5 :  0.0011023521656170487\n",
      "time taken :  0.5729489326477051\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.052161503583192825\n",
      "lossl :  0.0 loss1 :  0.0002896309015341103 loss2 :  0.0012513160472735763 loss3 :  0.045320939272642136\n",
      "loss4 :  0.0035465718246996403 loss5 :  0.00175304408185184\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00812311191111803\n",
      "lossl :  0.0 loss1 :  0.00011882781836902723 loss2 :  0.004181861877441406 loss3 :  0.0015927314525470138\n",
      "loss4 :  0.0007185935974121094 loss5 :  0.0015110969543457031\n",
      "Iteration :  7  /  7\n",
      "loss :  0.012391281314194202\n",
      "lossl :  0.0 loss1 :  2.632141149661038e-05 loss2 :  0.0021664618980139494 loss3 :  0.0005116462707519531\n",
      "loss4 :  0.0071311951614916325 loss5 :  0.0025556564796715975\n",
      "time taken :  0.17742562294006348\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 85/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.009513474069535732\n",
      "lossl :  0.0 loss1 :  0.0014604568714275956 loss2 :  0.00013170242891646922 loss3 :  0.006550312042236328\n",
      "loss4 :  0.00017232894606422633 loss5 :  0.00119867327157408\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07515764236450195\n",
      "lossl :  7.629394644936838e-07 loss1 :  8.392333984375e-05 loss2 :  0.001260471297428012 loss3 :  0.0008541106944903731\n",
      "loss4 :  0.0683535560965538 loss5 :  0.004604816436767578\n",
      "Iteration :  7  /  7\n",
      "loss :  0.023143863305449486\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0012729645241051912 loss2 :  0.001367855118587613 loss3 :  0.006429624743759632\n",
      "loss4 :  0.011223554611206055 loss5 :  0.002849674317985773\n",
      "time taken :  0.5677721500396729\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.024521352723240852\n",
      "lossl :  1.5258789289873675e-06 loss1 :  0.00079259870108217 loss2 :  0.002821254776790738 loss3 :  0.005077552981674671\n",
      "loss4 :  0.008932018652558327 loss5 :  0.006896400358527899\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1294921338558197\n",
      "lossl :  0.0 loss1 :  0.044538117945194244 loss2 :  0.00010843276686500758 loss3 :  0.006960487458854914\n",
      "loss4 :  0.02948126755654812 loss5 :  0.04840383678674698\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05176877975463867\n",
      "lossl :  0.0 loss1 :  0.0005324363592080772 loss2 :  0.0008991241338662803 loss3 :  0.04478883743286133\n",
      "loss4 :  0.0020513534545898438 loss5 :  0.0034970282576978207\n",
      "time taken :  0.17662954330444336\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 86/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01733694039285183\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00344257359392941 loss2 :  0.0059257508255541325 loss3 :  0.0006117820739746094\n",
      "loss4 :  0.0026574134826660156 loss5 :  0.004699230194091797\n",
      "Iteration :  4  /  7\n",
      "loss :  0.016504287719726562\n",
      "lossl :  0.0 loss1 :  0.00022878646268509328 loss2 :  0.008147811517119408 loss3 :  0.0006959915044717491\n",
      "loss4 :  0.0009938239818438888 loss5 :  0.006437873933464289\n",
      "Iteration :  7  /  7\n",
      "loss :  0.037144407629966736\n",
      "lossl :  0.0 loss1 :  0.000420045864302665 loss2 :  0.03508280590176582 loss3 :  0.00020284652418922633\n",
      "loss4 :  4.119873119634576e-05 loss5 :  0.0013975143665447831\n",
      "time taken :  0.5723080635070801\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002699184464290738\n",
      "lossl :  0.0 loss1 :  3.814697265625e-05 loss2 :  0.0008599281427450478 loss3 :  0.0009248733404092491\n",
      "loss4 :  0.0005078315734863281 loss5 :  0.0003684043767862022\n",
      "Iteration :  4  /  7\n",
      "loss :  0.02027425728738308\n",
      "lossl :  0.0 loss1 :  0.0007157325744628906 loss2 :  0.0022254944778978825 loss3 :  0.004678535275161266\n",
      "loss4 :  0.011006355285644531 loss5 :  0.0016481399070471525\n",
      "Iteration :  7  /  7\n",
      "loss :  0.33681541681289673\n",
      "lossl :  0.0 loss1 :  0.20460844039916992 loss2 :  0.012089729309082031 loss3 :  0.012159347534179688\n",
      "loss4 :  0.0388798713684082 loss5 :  0.06907801330089569\n",
      "time taken :  0.1753251552581787\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 87/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.023296356201171875\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.02086620405316353 loss2 :  0.0009915351402014494 loss3 :  0.00017623901658225805\n",
      "loss4 :  0.0010128021240234375 loss5 :  0.0002493858337402344\n",
      "Iteration :  4  /  7\n",
      "loss :  0.06775961071252823\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0001392364501953125 loss2 :  0.048341941088438034 loss3 :  0.0005794524913653731\n",
      "loss4 :  0.0021178245078772306 loss5 :  0.016581058502197266\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01534566842019558\n",
      "lossl :  0.0 loss1 :  0.00010795592970680445 loss2 :  0.0018626212840899825 loss3 :  0.00034608840360306203\n",
      "loss4 :  0.0050490377470850945 loss5 :  0.007979964837431908\n",
      "time taken :  0.5737032890319824\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01390385627746582\n",
      "lossl :  0.0 loss1 :  0.00016326904005836695 loss2 :  0.0031817436683923006 loss3 :  0.004294681362807751\n",
      "loss4 :  0.003412437392398715 loss5 :  0.002851724624633789\n",
      "Iteration :  4  /  7\n",
      "loss :  0.008871269412338734\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.0004048347473144531 loss2 :  0.0005589484935626388 loss3 :  0.0014680862659588456\n",
      "loss4 :  0.0010498047340661287 loss5 :  0.0053882598876953125\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01122741773724556\n",
      "lossl :  0.0 loss1 :  0.0002692222478799522 loss2 :  0.00039358140202239156 loss3 :  0.0005001068348065019\n",
      "loss4 :  0.0008873939514160156 loss5 :  0.009177112951874733\n",
      "time taken :  0.17772889137268066\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 88/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.14047059416770935\n",
      "lossl :  3.52859501617786e-06 loss1 :  0.0028394698165357113 loss2 :  0.006690406706184149 loss3 :  0.006607246585190296\n",
      "loss4 :  0.03697767108678818 loss5 :  0.08735227584838867\n",
      "Iteration :  4  /  7\n",
      "loss :  0.062447961419820786\n",
      "lossl :  4.482269105210435e-06 loss1 :  0.0002593040408100933 loss2 :  0.020777404308319092 loss3 :  0.0006443023448809981\n",
      "loss4 :  0.029273366555571556 loss5 :  0.01148910541087389\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005093669984489679\n",
      "lossl :  0.0 loss1 :  2.593994213384576e-05 loss2 :  3.452301098150201e-05 loss3 :  0.00026416778564453125\n",
      "loss4 :  0.004353618714958429 loss5 :  0.0004154205380473286\n",
      "time taken :  0.5753989219665527\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.007941151037812233\n",
      "lossl :  0.0 loss1 :  0.0002860069216694683 loss2 :  0.000446319580078125 loss3 :  0.0028612136375159025\n",
      "loss4 :  0.0032139779068529606 loss5 :  0.0011336326133459806\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007165051065385342\n",
      "lossl :  0.0 loss1 :  2.059936559817288e-05 loss2 :  0.0026929855812340975 loss3 :  0.001116085099056363\n",
      "loss4 :  0.0025937079917639494 loss5 :  0.0007416725275106728\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1107025146484375\n",
      "lossl :  0.0 loss1 :  0.02482633665204048 loss2 :  0.00026979445829056203 loss3 :  0.002916526747867465\n",
      "loss4 :  0.033361148089170456 loss5 :  0.04932870715856552\n",
      "time taken :  0.17672491073608398\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 89/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0338016040623188\n",
      "lossl :  0.0 loss1 :  0.00012531279935501516 loss2 :  0.002031183335930109 loss3 :  0.0013274193042889237\n",
      "loss4 :  0.023932266980409622 loss5 :  0.006385421846061945\n",
      "Iteration :  4  /  7\n",
      "loss :  1.4710313081741333\n",
      "lossl :  0.0 loss1 :  0.0014395713806152344 loss2 :  0.6447605490684509 loss3 :  0.6011666059494019\n",
      "loss4 :  0.005411386489868164 loss5 :  0.2182532250881195\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07468519359827042\n",
      "lossl :  2.1839141481905244e-05 loss1 :  0.000412416469771415 loss2 :  0.002274751663208008 loss3 :  0.012779283337295055\n",
      "loss4 :  0.006319379899650812 loss5 :  0.0528775230050087\n",
      "time taken :  0.648003101348877\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00793919526040554\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.0008734703296795487 loss2 :  0.0004934311145916581 loss3 :  0.005848932079970837\n",
      "loss4 :  0.00017871856107376516 loss5 :  0.0005433082696981728\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0060980794951319695\n",
      "lossl :  0.0 loss1 :  7.64846772653982e-05 loss2 :  0.0007763862377032638 loss3 :  0.0015281677478924394\n",
      "loss4 :  0.001943874405696988 loss5 :  0.0017731667030602694\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  7  /  7\n",
      "loss :  0.19440770149230957\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.02377314493060112 loss2 :  0.052590180188417435 loss3 :  0.014050960540771484\n",
      "loss4 :  0.10206727683544159 loss5 :  0.0019259452819824219\n",
      "time taken :  0.200819730758667\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 90/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.001796626951545477\n",
      "lossl :  0.0 loss1 :  0.00029077529325149953 loss2 :  0.00016307830810546875 loss3 :  2.937316821771674e-05\n",
      "loss4 :  0.0010597228538244963 loss5 :  0.0002536773681640625\n",
      "Iteration :  4  /  7\n",
      "loss :  0.12100858986377716\n",
      "lossl :  0.0 loss1 :  0.0978008285164833 loss2 :  0.0017671107780188322 loss3 :  0.019796038046479225\n",
      "loss4 :  0.0015940666198730469 loss5 :  5.054473876953125e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005246353335678577\n",
      "lossl :  0.0 loss1 :  1.182556115963962e-05 loss2 :  0.0005634307744912803 loss3 :  0.0013422966003417969\n",
      "loss4 :  0.0007971763843670487 loss5 :  0.0025316239334642887\n",
      "time taken :  0.5809710025787354\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0024131773971021175\n",
      "lossl :  0.0 loss1 :  4.863739013671875e-05 loss2 :  0.0013188362354412675 loss3 :  0.0003247261047363281\n",
      "loss4 :  0.00044183729914948344 loss5 :  0.00027914048405364156\n",
      "Iteration :  4  /  7\n",
      "loss :  0.018265342339873314\n",
      "lossl :  1.716613724056515e-06 loss1 :  0.011065769009292126 loss2 :  0.0018477439880371094 loss3 :  0.0004994392511434853\n",
      "loss4 :  0.0030840872786939144 loss5 :  0.0017665863269940019\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01638331450521946\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0015645980602130294 loss2 :  0.00044317246647551656 loss3 :  0.012386465445160866\n",
      "loss4 :  0.00019321442232467234 loss5 :  0.0017957687377929688\n",
      "time taken :  0.16498613357543945\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 91/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04628939926624298\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0002898216189350933 loss2 :  0.0014485359424725175 loss3 :  0.008890390396118164\n",
      "loss4 :  0.015209722332656384 loss5 :  0.020450735464692116\n",
      "Iteration :  4  /  7\n",
      "loss :  1.6808252334594727\n",
      "lossl :  4.19616708313697e-06 loss1 :  0.004149913787841797 loss2 :  0.4897085130214691 loss3 :  0.12738895416259766\n",
      "loss4 :  0.04012908786535263 loss5 :  1.0194445848464966\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007683921605348587\n",
      "lossl :  0.0 loss1 :  0.00011167526099598035 loss2 :  0.0046128989197313786 loss3 :  0.0015570640098303556\n",
      "loss4 :  0.0003124237118754536 loss5 :  0.0010898590553551912\n",
      "time taken :  0.656700849533081\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0035669326316565275\n",
      "lossl :  0.0 loss1 :  0.00016679763211868703 loss2 :  0.001199436141178012 loss3 :  0.001269435859285295\n",
      "loss4 :  0.0005972862127237022 loss5 :  0.00033397675724700093\n",
      "Iteration :  4  /  7\n",
      "loss :  0.026508474722504616\n",
      "lossl :  0.0 loss1 :  0.00015907287888694555 loss2 :  0.011230086907744408 loss3 :  0.010007476434111595\n",
      "loss4 :  0.0009043693426065147 loss5 :  0.0042074681259691715\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0003885268815793097\n",
      "lossl :  0.0 loss1 :  8.96453821042087e-06 loss2 :  4.844665454584174e-05 loss3 :  9.689330909168348e-05\n",
      "loss4 :  2.307891918462701e-05 loss5 :  0.00021114348783157766\n",
      "time taken :  0.19327855110168457\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 92/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.24016600847244263\n",
      "lossl :  0.0 loss1 :  0.0 loss2 :  0.19362230598926544 loss3 :  0.003027629805728793\n",
      "loss4 :  1.640319896978326e-05 loss5 :  0.0434996597468853\n",
      "Iteration :  4  /  7\n",
      "loss :  0.011019755154848099\n",
      "lossl :  1.716613724056515e-06 loss1 :  0.00015473365783691406 loss2 :  0.008647823706269264 loss3 :  0.00013589859008789062\n",
      "loss4 :  0.0012499808799475431 loss5 :  0.0008296013111248612\n",
      "Iteration :  7  /  7\n",
      "loss :  0.15509004890918732\n",
      "lossl :  7.05719003235572e-06 loss1 :  1.068115216185106e-05 loss2 :  0.09151019901037216 loss3 :  0.015022754669189453\n",
      "loss4 :  0.00013446807861328125 loss5 :  0.04840488359332085\n",
      "time taken :  0.5987100601196289\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0012324333656579256\n",
      "lossl :  0.0 loss1 :  0.00010137558274436742 loss2 :  0.00014657973952125758 loss3 :  0.0006585121154785156\n",
      "loss4 :  0.00013399124145507812 loss5 :  0.00019197464280296117\n",
      "Iteration :  4  /  7\n",
      "loss :  0.04212489351630211\n",
      "lossl :  1.430511474609375e-06 loss1 :  7.581710815429688e-05 loss2 :  0.0053765298798680305 loss3 :  0.0029837607871741056\n",
      "loss4 :  0.033641766756772995 loss5 :  4.558563159662299e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007260275073349476\n",
      "lossl :  0.0 loss1 :  0.0034342766739428043 loss2 :  0.00031466485233977437 loss3 :  0.0007251739734783769\n",
      "loss4 :  0.0008826255798339844 loss5 :  0.001903533935546875\n",
      "time taken :  0.16963481903076172\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 93/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010743379592895508\n",
      "lossl :  0.0 loss1 :  0.0012970924144610763 loss2 :  0.00012998581223655492 loss3 :  0.0065310001373291016\n",
      "loss4 :  0.0010695457458496094 loss5 :  0.00171575543936342\n",
      "Iteration :  4  /  7\n",
      "loss :  0.12010421603918076\n",
      "lossl :  1.9073486612342094e-07 loss1 :  9.088516526389867e-05 loss2 :  0.00016779899306129664 loss3 :  0.042473506182432175\n",
      "loss4 :  0.0017444133991375566 loss5 :  0.07562742382287979\n",
      "Iteration :  7  /  7\n",
      "loss :  0.029332635924220085\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0021598816383630037 loss2 :  0.0015529632801190019 loss3 :  0.0003127098025288433\n",
      "loss4 :  0.025200461968779564 loss5 :  0.00010643005225574598\n",
      "time taken :  0.5638854503631592\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0018064499599859118\n",
      "lossl :  0.0 loss1 :  3.1948089599609375e-05 loss2 :  0.00030460357083939016 loss3 :  0.00040454865666106343\n",
      "loss4 :  0.000982570694759488 loss5 :  8.277893357444555e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010099315084517002\n",
      "lossl :  0.0 loss1 :  0.00017032623873092234 loss2 :  0.0001241683930857107 loss3 :  0.0006821632268838584\n",
      "loss4 :  0.0008208274957723916 loss5 :  0.008301829919219017\n",
      "Iteration :  7  /  7\n",
      "loss :  0.002160024596378207\n",
      "lossl :  0.0 loss1 :  0.00015130042447708547 loss2 :  0.00019044875807594508 loss3 :  0.0007962227100506425\n",
      "loss4 :  0.0007162094116210938 loss5 :  0.00030584336491301656\n",
      "time taken :  0.16561532020568848\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 94/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.018968725576996803\n",
      "lossl :  0.0 loss1 :  0.003413581755012274 loss2 :  0.00034990310086868703 loss3 :  0.0006290435558184981\n",
      "loss4 :  0.0013155937194824219 loss5 :  0.013260602951049805\n",
      "Iteration :  4  /  7\n",
      "loss :  0.012940788641571999\n",
      "lossl :  0.0 loss1 :  0.00014009475125931203 loss2 :  0.011318874545395374 loss3 :  0.0013763427268713713\n",
      "loss4 :  6.942749314475805e-05 loss5 :  3.604888843256049e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.31699737906455994\n",
      "lossl :  0.0 loss1 :  0.008248996920883656 loss2 :  0.005804920103400946 loss3 :  0.00038814544677734375\n",
      "loss4 :  0.30188220739364624 loss5 :  0.0006731033208779991\n",
      "time taken :  0.5709519386291504\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.015958309173583984\n",
      "lossl :  0.0 loss1 :  3.223419116693549e-05 loss2 :  0.00013608932204078883 loss3 :  0.00034351350041106343\n",
      "loss4 :  0.000652313232421875 loss5 :  0.01479415874928236\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005083656404167414\n",
      "lossl :  0.0 loss1 :  0.0007407188531942666 loss2 :  6.0558319091796875e-05 loss3 :  0.00023708344087935984\n",
      "loss4 :  0.0012464523315429688 loss5 :  0.0027988434303551912\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0009219169151037931\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.00011587142944335938 loss2 :  0.00012445449829101562 loss3 :  0.00030984877957962453\n",
      "loss4 :  0.00012331009202171117 loss5 :  0.0002483367861714214\n",
      "time taken :  0.17789244651794434\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 95/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.015753841027617455\n",
      "lossl :  0.0 loss1 :  0.010295772925019264 loss2 :  7.63893112889491e-05 loss3 :  0.00020713805861305445\n",
      "loss4 :  0.005119609646499157 loss5 :  5.493163916980848e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006761359982192516\n",
      "lossl :  0.0 loss1 :  0.0007231712224893272 loss2 :  0.00040073395939543843 loss3 :  5.2928924560546875e-05\n",
      "loss4 :  0.00037088393582962453 loss5 :  0.005213642027229071\n",
      "Iteration :  7  /  7\n",
      "loss :  0.002197170164436102\n",
      "lossl :  0.0 loss1 :  0.0010153769981116056 loss2 :  0.00020742416381835938 loss3 :  0.0007954597240313888\n",
      "loss4 :  7.99179106252268e-05 loss5 :  9.89913969533518e-05\n",
      "time taken :  0.5783216953277588\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002788591431453824\n",
      "lossl :  0.0 loss1 :  0.0015507697826251388 loss2 :  0.0004958629724569619 loss3 :  0.00034809112548828125\n",
      "loss4 :  4.882812572759576e-05 loss5 :  0.00034503935603424907\n",
      "Iteration :  4  /  7\n",
      "loss :  0.001525401952676475\n",
      "lossl :  0.0 loss1 :  0.00011405944678699598 loss2 :  0.00026798248291015625 loss3 :  0.0007539748912677169\n",
      "loss4 :  0.0001394271821482107 loss5 :  0.0002499580441508442\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0012745857238769531\n",
      "lossl :  0.0 loss1 :  1.8405913579044864e-05 loss2 :  0.00012159347534179688 loss3 :  0.00019721985154319555\n",
      "loss4 :  0.0007328033680096269 loss5 :  0.00020456314086914062\n",
      "time taken :  0.17609262466430664\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 96/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.006863260641694069\n",
      "lossl :  0.0 loss1 :  0.0001762867032084614 loss2 :  0.0052468301728367805 loss3 :  0.00047740936861373484\n",
      "loss4 :  0.0006005287286825478 loss5 :  0.00036220549372956157\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005848502740263939\n",
      "lossl :  0.0 loss1 :  2.555847095209174e-05 loss2 :  8.56399565236643e-05 loss3 :  0.0004408836248330772\n",
      "loss4 :  0.004670524504035711 loss5 :  0.0006258964422158897\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0024891854263842106\n",
      "lossl :  0.0 loss1 :  0.00014314652071334422 loss2 :  2.803802453854587e-05 loss3 :  0.00164794921875\n",
      "loss4 :  0.00024299621873069555 loss5 :  0.00042705534724518657\n",
      "time taken :  0.5701484680175781\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07210907340049744\n",
      "lossl :  2.8610230629055877e-07 loss1 :  1.4591217222914565e-05 loss2 :  0.01298513449728489 loss3 :  0.00443191546946764\n",
      "loss4 :  0.05447850376367569 loss5 :  0.00019865036301780492\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0021865845192223787\n",
      "lossl :  0.0 loss1 :  0.0001314163237111643 loss2 :  0.00038022996159270406 loss3 :  0.0009009361383505166\n",
      "loss4 :  0.000690460205078125 loss5 :  8.354186866199598e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0007339477888308465\n",
      "lossl :  0.0 loss1 :  7.476806786144152e-05 loss2 :  9.212493750965223e-05 loss3 :  0.00023126602172851562\n",
      "loss4 :  0.0002610206720419228 loss5 :  7.476806786144152e-05\n",
      "time taken :  0.17811346054077148\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 97/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.028885463252663612\n",
      "lossl :  0.0 loss1 :  5.53131121705519e-06 loss2 :  0.026058007031679153 loss3 :  0.0002754211309365928\n",
      "loss4 :  5.044937279308215e-05 loss5 :  0.002496051834896207\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003955650143325329\n",
      "lossl :  0.0 loss1 :  0.0020530701149255037 loss2 :  0.00029506682767532766 loss3 :  0.0003589630068745464\n",
      "loss4 :  0.0003811836359091103 loss5 :  0.0008673667907714844\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003977751825004816\n",
      "lossl :  0.0 loss1 :  0.0011916875373572111 loss2 :  4.825591895496473e-05 loss3 :  0.0004276275576557964\n",
      "loss4 :  0.0017031669849529862 loss5 :  0.0006070137023925781\n",
      "time taken :  0.5685322284698486\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0015670775901526213\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.00048532485379837453 loss2 :  2.765655517578125e-05 loss3 :  0.00086889264639467\n",
      "loss4 :  9.994507126975805e-05 loss5 :  8.487701416015625e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0012653351295739412\n",
      "lossl :  0.0 loss1 :  3.700256274896674e-05 loss2 :  0.0007605552673339844 loss3 :  0.0001508712739450857\n",
      "loss4 :  0.00020542144193314016 loss5 :  0.00011148452904308215\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08670053631067276\n",
      "lossl :  1.9073486612342094e-07 loss1 :  2.9468536013155244e-05 loss2 :  0.018387842923402786 loss3 :  0.007764053530991077\n",
      "loss4 :  0.05983233451843262 loss5 :  0.0006866455078125\n",
      "time taken :  0.18117737770080566\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 98/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.37755855917930603\n",
      "lossl :  0.0 loss1 :  0.37680336833000183 loss2 :  1.201629675051663e-05 loss3 :  9.689330909168348e-05\n",
      "loss4 :  0.0003106117364950478 loss5 :  0.000335693359375\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007035636808723211\n",
      "lossl :  1.144409225162235e-06 loss1 :  0.000567531562410295 loss2 :  0.003882121993228793 loss3 :  0.0007868766551837325\n",
      "loss4 :  0.00013093948655296117 loss5 :  0.001667022705078125\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01348800677806139\n",
      "lossl :  0.0 loss1 :  0.0009757995721884072 loss2 :  0.0002629280206747353 loss3 :  0.0005905151483602822\n",
      "loss4 :  0.004408836364746094 loss5 :  0.007249927613884211\n",
      "time taken :  0.5674037933349609\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0018666267860680819\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0001123428373830393 loss2 :  0.000592136406339705 loss3 :  0.00017032623873092234\n",
      "loss4 :  8.296966552734375e-05 loss5 :  0.0009085655328817666\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0037622926756739616\n",
      "lossl :  0.0 loss1 :  0.0015816688537597656 loss2 :  7.22885160939768e-05 loss3 :  0.0014994144439697266\n",
      "loss4 :  0.0005441665416583419 loss5 :  6.47544875391759e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0008289337274618447\n",
      "lossl :  0.0 loss1 :  5.187988426769152e-05 loss2 :  2.365112231927924e-05 loss3 :  6.446838233387098e-05\n",
      "loss4 :  0.00022954940504860133 loss5 :  0.0004593849298544228\n",
      "time taken :  0.17712783813476562\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 99/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01138992328196764\n",
      "lossl :  2.670288040462765e-06 loss1 :  0.0009933471446856856 loss2 :  0.003784275148063898 loss3 :  0.0044536590576171875\n",
      "loss4 :  0.0015109062660485506 loss5 :  0.0006450653309002519\n",
      "Iteration :  4  /  7\n",
      "loss :  0.051906682550907135\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.015481042675673962 loss2 :  0.0008438110235147178 loss3 :  0.014624166302382946\n",
      "loss4 :  0.0011947632301598787 loss5 :  0.019762802869081497\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006633663550019264\n",
      "lossl :  0.0 loss1 :  0.00529823312535882 loss2 :  0.00043363572331145406 loss3 :  0.0003925323544535786\n",
      "loss4 :  4.062652442371473e-05 loss5 :  0.00046863555326126516\n",
      "time taken :  0.5664551258087158\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008428191766142845\n",
      "lossl :  0.0 loss1 :  0.00011749267287086695 loss2 :  0.0004525184631347656 loss3 :  0.0009784698486328125\n",
      "loss4 :  0.006786250974982977 loss5 :  9.34600830078125e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.002876377198845148\n",
      "lossl :  0.0 loss1 :  0.00025539397029206157 loss2 :  0.00047941209049895406 loss3 :  0.00015726088895462453\n",
      "loss4 :  0.0007073402521200478 loss5 :  0.0012769699096679688\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004571819677948952\n",
      "lossl :  0.0 loss1 :  0.0006954193231649697 loss2 :  0.0012859344715252519 loss3 :  0.0022085190284997225\n",
      "loss4 :  0.00023126602172851562 loss5 :  0.0001506805419921875\n",
      "time taken :  0.16717934608459473\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 100/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.009255124256014824\n",
      "lossl :  0.0 loss1 :  9.880065772449598e-05 loss2 :  0.006097030825912952 loss3 :  0.002837657928466797\n",
      "loss4 :  0.00011672973778331652 loss5 :  0.0001049041748046875\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01169581338763237\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0010557651985436678 loss2 :  0.003532028291374445 loss3 :  0.004210376646369696\n",
      "loss4 :  0.002051448915153742 loss5 :  0.00084600446280092\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03320889174938202\n",
      "lossl :  2.28881845032447e-06 loss1 :  0.001475000404752791 loss2 :  0.005698489956557751 loss3 :  0.001135158585384488\n",
      "loss4 :  0.024828623980283737 loss5 :  6.933211989235133e-05\n",
      "time taken :  0.5580039024353027\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04978065565228462\n",
      "lossl :  0.0 loss1 :  0.007872867397964 loss2 :  8.039474778342992e-05 loss3 :  0.006473255343735218\n",
      "loss4 :  0.016053009778261185 loss5 :  0.019301127642393112\n",
      "Iteration :  4  /  7\n",
      "loss :  0.011935424990952015\n",
      "lossl :  0.0 loss1 :  0.007584571838378906 loss2 :  0.0023097037337720394 loss3 :  0.0005195617559365928\n",
      "loss4 :  0.0008804321405477822 loss5 :  0.0006411552312783897\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5605562329292297\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0002674102724995464 loss2 :  0.1139061227440834 loss3 :  0.05611696094274521\n",
      "loss4 :  0.3900241255760193 loss5 :  0.00024147033400367945\n",
      "time taken :  0.16591501235961914\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 101/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.012773990631103516\n",
      "lossl :  0.0 loss1 :  0.00010528564598644152 loss2 :  0.00350017542950809 loss3 :  0.0009001732105389237\n",
      "loss4 :  0.007691001985222101 loss5 :  0.0005773544544354081\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0025586129631847143\n",
      "lossl :  0.0 loss1 :  0.00015668869309592992 loss2 :  0.0011703490745276213 loss3 :  9.183883958030492e-05\n",
      "loss4 :  0.0004347801150288433 loss5 :  0.0007049560663290322\n",
      "Iteration :  7  /  7\n",
      "loss :  1.5075902938842773\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.004838800523430109 loss2 :  0.15591058135032654 loss3 :  0.11533336341381073\n",
      "loss4 :  0.05660538747906685 loss5 :  1.1749019622802734\n",
      "time taken :  0.5662014484405518\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005620479583740234\n",
      "lossl :  0.0 loss1 :  0.00011558532423805445 loss2 :  0.0004131317255087197 loss3 :  0.00037784577580168843\n",
      "loss4 :  0.002956962678581476 loss5 :  0.001756954239681363\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07857032120227814\n",
      "lossl :  0.0 loss1 :  0.013763380236923695 loss2 :  0.0007490158313885331 loss3 :  0.003570079803466797\n",
      "loss4 :  0.03170518949627876 loss5 :  0.02878265455365181\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004627036862075329\n",
      "lossl :  0.0 loss1 :  0.00037097930908203125 loss2 :  0.00019083023653365672 loss3 :  0.002600383711978793\n",
      "loss4 :  0.0012021064758300781 loss5 :  0.0002627372741699219\n",
      "time taken :  0.16576361656188965\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 102/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.003909778781235218\n",
      "lossl :  0.0 loss1 :  2.0313262211857364e-05 loss2 :  0.0005460738902911544 loss3 :  0.0006289482116699219\n",
      "loss4 :  0.0003371238708496094 loss5 :  0.0023773193825036287\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007765579037368298\n",
      "lossl :  0.0 loss1 :  2.384185791015625e-05 loss2 :  0.00019359588623046875 loss3 :  0.0036732673179358244\n",
      "loss4 :  0.0004957198980264366 loss5 :  0.0033791542518883944\n",
      "Iteration :  7  /  7\n",
      "loss :  0.023173045367002487\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0007827758672647178 loss2 :  0.002427387284114957 loss3 :  0.0009892464149743319\n",
      "loss4 :  0.01877412758767605 loss5 :  0.00019931793212890625\n",
      "time taken :  0.5617618560791016\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010361362248659134\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0033281086944043636 loss2 :  0.00034580231294967234 loss3 :  0.004228115081787109\n",
      "loss4 :  0.0021974563132971525 loss5 :  0.0002616882266011089\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005436754319816828\n",
      "lossl :  0.0 loss1 :  0.0037085055373609066 loss2 :  0.0007369041559286416 loss3 :  0.0004750251828227192\n",
      "loss4 :  0.00013217926607467234 loss5 :  0.0003841400030069053\n",
      "Iteration :  7  /  7\n",
      "loss :  0.000683212245348841\n",
      "lossl :  0.0 loss1 :  8.077621168922633e-05 loss2 :  0.0001508712739450857 loss3 :  0.00013866423978470266\n",
      "loss4 :  0.00016908645920921117 loss5 :  0.00014381408982444555\n",
      "time taken :  0.16486382484436035\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 103/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008716011419892311\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.001636505126953125 loss2 :  0.00026988983154296875 loss3 :  0.003198337508365512\n",
      "loss4 :  0.003300285432487726 loss5 :  0.0003106117364950478\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00513534527271986\n",
      "lossl :  0.0 loss1 :  1.3351440202313825e-06 loss2 :  0.0021195411682128906 loss3 :  0.0005092620849609375\n",
      "loss4 :  0.00012264252291060984 loss5 :  0.002382564591243863\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0030135156121104956\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0006221771473065019 loss2 :  0.0006513595581054688 loss3 :  0.0002058029203908518\n",
      "loss4 :  0.0010272025829181075 loss5 :  0.0005065918085165322\n",
      "time taken :  0.5596997737884521\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.030293798074126244\n",
      "lossl :  0.0 loss1 :  0.0074236392974853516 loss2 :  0.00019741058349609375 loss3 :  0.0016034126747399569\n",
      "loss4 :  0.010832691565155983 loss5 :  0.010236645117402077\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00026149750920012593\n",
      "lossl :  0.0 loss1 :  7.152557373046875e-06 loss2 :  2.422332727292087e-05 loss3 :  3.776550147449598e-05\n",
      "loss4 :  6.179809861350805e-05 loss5 :  0.00013055800809524953\n",
      "Iteration :  7  /  7\n",
      "loss :  0.002482175827026367\n",
      "lossl :  0.0 loss1 :  0.001417779945768416 loss2 :  0.00020742416381835938 loss3 :  8.831024024402723e-05\n",
      "loss4 :  0.0005658149602822959 loss5 :  0.00020284652418922633\n",
      "time taken :  0.16795611381530762\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 104/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1678110808134079\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.007553005125373602 loss2 :  0.05899615213274956 loss3 :  0.09706051647663116\n",
      "loss4 :  0.0010747909545898438 loss5 :  0.003126430558040738\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006956195924431086\n",
      "lossl :  0.0 loss1 :  0.00020627975754905492 loss2 :  0.000798130058683455 loss3 :  0.0015659332275390625\n",
      "loss4 :  8.354186866199598e-05 loss5 :  0.004302310757339001\n",
      "Iteration :  7  /  7\n",
      "loss :  0.3662850558757782\n",
      "lossl :  0.0 loss1 :  0.0006958961603231728 loss2 :  0.002850151155143976 loss3 :  0.015731047838926315\n",
      "loss4 :  0.0163100715726614 loss5 :  0.3306978940963745\n",
      "time taken :  0.5622484683990479\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.3741026818752289\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.00013380050950217992 loss2 :  0.10227413475513458 loss3 :  0.029208850115537643\n",
      "loss4 :  0.2423199713230133 loss5 :  0.00016565322584938258\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0040607452392578125\n",
      "lossl :  0.0 loss1 :  0.0026873587630689144 loss2 :  0.0003314018249511719 loss3 :  0.00012454987154342234\n",
      "loss4 :  0.0005867004510946572 loss5 :  0.0003307342412881553\n",
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      "loss :  0.002480888506397605\n",
      "lossl :  0.0 loss1 :  0.0003201484796591103 loss2 :  0.0002830505254678428 loss3 :  0.0013206482399255037\n",
      "loss4 :  0.00017652512178756297 loss5 :  0.00038051605224609375\n",
      "time taken :  0.1720137596130371\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 105/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.005527114495635033\n",
      "lossl :  0.0 loss1 :  0.00033817291841842234 loss2 :  0.0022618293296545744 loss3 :  4.329681542003527e-05\n",
      "loss4 :  0.0007826805231161416 loss5 :  0.0021011352073401213\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005649852100759745\n",
      "lossl :  0.0 loss1 :  8.897781663108617e-05 loss2 :  0.0054954527877271175 loss3 :  1.564025842526462e-05\n",
      "loss4 :  9.91821252682712e-06 loss5 :  3.986358569818549e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0635749101638794\n",
      "lossl :  0.0 loss1 :  0.02887265756726265 loss2 :  0.001131248427554965 loss3 :  0.012895584106445312\n",
      "loss4 :  0.01909632608294487 loss5 :  0.0015790939796715975\n",
      "time taken :  0.5779554843902588\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0641104206442833\n",
      "lossl :  0.0 loss1 :  0.011507272720336914 loss2 :  0.0002929687616415322 loss3 :  0.003272152040153742\n",
      "loss4 :  0.02485942840576172 loss5 :  0.024178599938750267\n",
      "Iteration :  4  /  7\n",
      "loss :  0.36968129873275757\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0021508694626390934 loss2 :  0.11445870250463486 loss3 :  0.030369948595762253\n",
      "loss4 :  0.22246575355529785 loss5 :  0.00023574828810524195\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003136157989501953\n",
      "lossl :  0.0 loss1 :  3.1757354008732364e-05 loss2 :  0.0010485649108886719 loss3 :  0.0015031814109534025\n",
      "loss4 :  0.0001049995407811366 loss5 :  0.00044765471830032766\n",
      "time taken :  0.1771693229675293\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 106/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008104229345917702\n",
      "lossl :  0.0 loss1 :  6.866455078125e-05 loss2 :  0.0004562377871479839 loss3 :  1.964569128176663e-05\n",
      "loss4 :  1.087188684323337e-05 loss5 :  0.007548809051513672\n",
      "Iteration :  4  /  7\n",
      "loss :  0.022782517597079277\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0033224106300622225 loss2 :  0.012088680639863014 loss3 :  0.0019156455527991056\n",
      "loss4 :  0.0018014907836914062 loss5 :  0.003653907682746649\n",
      "Iteration :  7  /  7\n",
      "loss :  0.010397244244813919\n",
      "lossl :  2.098083541568485e-06 loss1 :  0.0001049041748046875 loss2 :  0.0023229599464684725 loss3 :  0.00034055710420943797\n",
      "loss4 :  0.0015643120277673006 loss5 :  0.006062412168830633\n",
      "time taken :  0.5706877708435059\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.38056933879852295\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0007479667547158897 loss2 :  0.14028966426849365 loss3 :  0.042345426976680756\n",
      "loss4 :  0.19703921675682068 loss5 :  0.0001468658447265625\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0024553299881517887\n",
      "lossl :  0.0 loss1 :  0.0002384185791015625 loss2 :  0.00039844511775299907 loss3 :  0.0003047943173442036\n",
      "loss4 :  0.0007681846618652344 loss5 :  0.0007454872247762978\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0057679652236402035\n",
      "lossl :  0.0 loss1 :  0.0027507306076586246 loss2 :  0.0004543304385151714 loss3 :  0.002067089080810547\n",
      "loss4 :  0.00013408661470748484 loss5 :  0.00036172865657135844\n",
      "time taken :  0.1790447235107422\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 107/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.027948904782533646\n",
      "lossl :  6.361007399391383e-05 loss1 :  0.008384657092392445 loss2 :  0.0012438774574548006 loss3 :  0.015006733126938343\n",
      "loss4 :  0.0020317076705396175 loss5 :  0.0012183189392089844\n",
      "Iteration :  4  /  7\n",
      "loss :  0.2540138363838196\n",
      "lossl :  0.0 loss1 :  0.00013036727614235133 loss2 :  0.00013408661470748484 loss3 :  0.06693611294031143\n",
      "loss4 :  0.12850666046142578 loss5 :  0.05830659717321396\n",
      "Iteration :  7  /  7\n",
      "loss :  0.387238472700119\n",
      "lossl :  0.0 loss1 :  0.0003104209899902344 loss2 :  0.3353702425956726 loss3 :  0.049985457211732864\n",
      "loss4 :  0.0005992889637127519 loss5 :  0.0009730338933877647\n",
      "time taken :  0.5710353851318359\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.009964276105165482\n",
      "lossl :  0.0 loss1 :  0.0003035545232705772 loss2 :  4.539489600574598e-05 loss3 :  0.00790948886424303\n",
      "loss4 :  0.0014727592933923006 loss5 :  0.0002330779971089214\n",
      "Iteration :  4  /  7\n",
      "loss :  0.23106902837753296\n",
      "lossl :  2.8610230629055877e-07 loss1 :  5.130767749506049e-05 loss2 :  0.10015912353992462 loss3 :  0.018630027770996094\n",
      "loss4 :  0.11219100654125214 loss5 :  3.728866431629285e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03461923450231552\n",
      "lossl :  0.0 loss1 :  0.005515670869499445 loss2 :  0.0006502151372842491 loss3 :  0.0017241478199139237\n",
      "loss4 :  0.01388254202902317 loss5 :  0.012846660800278187\n",
      "time taken :  0.18198299407958984\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 108/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0031214712653309107\n",
      "lossl :  0.0 loss1 :  4.367828296381049e-05 loss2 :  0.0006321907276287675 loss3 :  0.000293731689453125\n",
      "loss4 :  0.0009924888145178556 loss5 :  0.0011593818198889494\n",
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      "loss :  0.009489965625107288\n",
      "lossl :  0.0 loss1 :  0.000876951206009835 loss2 :  0.004024982452392578 loss3 :  0.0007047653198242188\n",
      "loss4 :  0.0005075454828329384 loss5 :  0.0033757209312170744\n",
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      "loss :  0.01400766335427761\n",
      "lossl :  0.0 loss1 :  0.0008306503295898438 loss2 :  0.0033024786971509457 loss3 :  0.0022418976295739412\n",
      "loss4 :  0.00014429092698264867 loss5 :  0.007488346192985773\n",
      "time taken :  0.5692257881164551\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.18038047850131989\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.003969383426010609 loss2 :  0.06646032631397247 loss3 :  0.015707015991210938\n",
      "loss4 :  0.08347249031066895 loss5 :  0.010771083645522594\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01145086344331503\n",
      "lossl :  0.0 loss1 :  0.0001352310209767893 loss2 :  0.0002461433468852192 loss3 :  0.009321784600615501\n",
      "loss4 :  0.0007642746204510331 loss5 :  0.0009834289085119963\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010432242415845394\n",
      "lossl :  0.0 loss1 :  0.00039691926212981343 loss2 :  0.00015096664719749242 loss3 :  9.641647193348035e-05\n",
      "loss4 :  0.00022583008103538305 loss5 :  0.00017309188842773438\n",
      "time taken :  0.17808985710144043\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 109/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0027409554459154606\n",
      "lossl :  0.0 loss1 :  1.5735626220703125e-05 loss2 :  3.662109520519152e-05 loss3 :  5.187988426769152e-05\n",
      "loss4 :  9.822845458984375e-05 loss5 :  0.002538490341976285\n",
      "Iteration :  4  /  7\n",
      "loss :  0.056256912648677826\n",
      "lossl :  0.0 loss1 :  0.0002286911039846018 loss2 :  0.013252305798232555 loss3 :  0.0005320549244061112\n",
      "loss4 :  0.008925390429794788 loss5 :  0.033318471163511276\n",
      "Iteration :  7  /  7\n",
      "loss :  0.010475635528564453\n",
      "lossl :  0.0 loss1 :  0.0009058952564373612 loss2 :  0.0014075279468670487 loss3 :  0.00250663748010993\n",
      "loss4 :  0.001238918281160295 loss5 :  0.00441665668040514\n",
      "time taken :  0.5912017822265625\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0005147934425622225\n",
      "lossl :  0.0 loss1 :  7.05719003235572e-06 loss2 :  0.00011358260962879285 loss3 :  0.00015182494826149195\n",
      "loss4 :  0.000102996826171875 loss5 :  0.00013933182344771922\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0006348133319988847\n",
      "lossl :  0.0 loss1 :  0.00041413307189941406 loss2 :  3.1757354008732364e-05 loss3 :  3.070831371587701e-05\n",
      "loss4 :  4.596710277837701e-05 loss5 :  0.00011224746413063258\n",
      "Iteration :  7  /  7\n",
      "loss :  0.012723732739686966\n",
      "lossl :  0.0 loss1 :  0.0021665573585778475 loss2 :  0.00014495849609375 loss3 :  0.0006058692815713584\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss4 :  0.005229282192885876 loss5 :  0.004577064421027899\n",
      "time taken :  0.18830537796020508\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 110/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0013335705734789371\n",
      "lossl :  0.0 loss1 :  6.50405854685232e-05 loss2 :  0.0004853725549764931 loss3 :  0.0005819320795126259\n",
      "loss4 :  0.00010337829735362902 loss5 :  9.784698340808973e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.06062579154968262\n",
      "lossl :  0.0 loss1 :  0.00469207763671875 loss2 :  0.010770034976303577 loss3 :  0.04243283346295357\n",
      "loss4 :  0.0009499549632892013 loss5 :  0.0017808914417400956\n",
      "Iteration :  7  /  7\n",
      "loss :  0.048033855855464935\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.0009968758095055819 loss2 :  0.021396398544311523 loss3 :  0.003210735274478793\n",
      "loss4 :  0.01575307920575142 loss5 :  0.006676101591438055\n",
      "time taken :  0.6958870887756348\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008268261328339577\n",
      "lossl :  0.0 loss1 :  0.0004996299976482987 loss2 :  3.814697265625e-06 loss3 :  0.005852603819221258\n",
      "loss4 :  0.000521755195222795 loss5 :  0.0013904571533203125\n",
      "Iteration :  4  /  7\n",
      "loss :  0.001027011894620955\n",
      "lossl :  0.0 loss1 :  0.0004941940424032509 loss2 :  5.3119660151423886e-05 loss3 :  0.00018644332885742188\n",
      "loss4 :  4.653930591302924e-05 loss5 :  0.00024671555729582906\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0005739211919717491\n",
      "lossl :  0.0 loss1 :  7.114410254871473e-05 loss2 :  0.0001145362839451991 loss3 :  8.478164818370715e-05\n",
      "loss4 :  0.00014352798461914062 loss5 :  0.00015993117995094508\n",
      "time taken :  0.2059190273284912\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 111/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008734512142837048\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0004445076046977192 loss2 :  0.0017860413063317537 loss3 :  0.0012187957763671875\n",
      "loss4 :  0.004967594053596258 loss5 :  0.00031719208345748484\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004683971405029297\n",
      "lossl :  0.0 loss1 :  0.001058292342349887 loss2 :  0.0007787704234942794 loss3 :  0.002487278077751398\n",
      "loss4 :  3.919601294910535e-05 loss5 :  0.0003204345703125\n",
      "Iteration :  7  /  7\n",
      "loss :  1.382693886756897\n",
      "lossl :  0.0 loss1 :  0.0002452850458212197 loss2 :  1.273390769958496 loss3 :  0.1044289618730545\n",
      "loss4 :  0.00030832289485260844 loss5 :  0.0043205260299146175\n",
      "time taken :  0.6731603145599365\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0008623123285360634\n",
      "lossl :  0.0 loss1 :  0.0003678321954794228 loss2 :  7.80105619924143e-05 loss3 :  0.0001585006684763357\n",
      "loss4 :  0.00018548965454101562 loss5 :  7.2479248046875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0001464843808207661\n",
      "lossl :  0.0 loss1 :  9.5367431640625e-07 loss2 :  3.0231476557673886e-05 loss3 :  5.3691863286076114e-05\n",
      "loss4 :  5.607604907709174e-05 loss5 :  5.53131121705519e-06\n",
      "Iteration :  7  /  7\n",
      "loss :  0.017115497961640358\n",
      "lossl :  0.0 loss1 :  0.00260753626935184 loss2 :  9.975433204090223e-05 loss3 :  0.000790500664152205\n",
      "loss4 :  0.007789325900375843 loss5 :  0.005828380584716797\n",
      "time taken :  0.18574190139770508\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 112/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03225889056921005\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0015062332386150956 loss2 :  0.022750090807676315 loss3 :  0.005887365434318781\n",
      "loss4 :  0.0011682987678796053 loss5 :  0.0009465217590332031\n",
      "Iteration :  4  /  7\n",
      "loss :  0.002598667284473777\n",
      "lossl :  0.0 loss1 :  4.5108794438419864e-05 loss2 :  0.00025691985501907766 loss3 :  0.0002957344113383442\n",
      "loss4 :  0.00183868408203125 loss5 :  0.00016222000704146922\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0006310463068075478\n",
      "lossl :  5.34057608092553e-06 loss1 :  0.00012655257887672633 loss2 :  0.0001470565766794607 loss3 :  8.19206252344884e-05\n",
      "loss4 :  0.00020246506028342992 loss5 :  6.771087646484375e-05\n",
      "time taken :  0.5779929161071777\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0009369849576614797\n",
      "lossl :  0.0 loss1 :  0.0005169868236407638 loss2 :  6.942749314475805e-05 loss3 :  0.00019292831711936742\n",
      "loss4 :  0.0001031875581247732 loss5 :  5.445480201160535e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00014896393986418843\n",
      "lossl :  0.0 loss1 :  1.316070574830519e-05 loss2 :  3.089904930675402e-05 loss3 :  1.258850079466356e-05\n",
      "loss4 :  2.6702880859375e-05 loss5 :  6.561279587913305e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0005050658946856856\n",
      "lossl :  0.0 loss1 :  0.00012483596219681203 loss2 :  0.00014562606520485133 loss3 :  3.337860107421875e-05\n",
      "loss4 :  5.474090721691027e-05 loss5 :  0.0001464843808207661\n",
      "time taken :  0.19173979759216309\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 113/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00335693359375\n",
      "lossl :  0.0 loss1 :  1.62124638336536e-06 loss2 :  4.692077709478326e-05 loss3 :  8.897781663108617e-05\n",
      "loss4 :  0.0032068253494799137 loss5 :  1.258850079466356e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.024553634226322174\n",
      "lossl :  0.0 loss1 :  0.00016126633272506297 loss2 :  0.001305913901887834 loss3 :  0.0016510009299963713\n",
      "loss4 :  0.00024881362332962453 loss5 :  0.02118663862347603\n",
      "Iteration :  7  /  7\n",
      "loss :  0.03835182264447212\n",
      "lossl :  1.9073486612342094e-07 loss1 :  5.8841706049861386e-05 loss2 :  0.01379613857716322 loss3 :  0.00010633468627929688\n",
      "loss4 :  0.0005753517034463584 loss5 :  0.023814965039491653\n",
      "time taken :  0.5908286571502686\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0026958466041833162\n",
      "lossl :  0.0 loss1 :  0.000828647636808455 loss2 :  0.00018825530423782766 loss3 :  0.0009157180902548134\n",
      "loss4 :  0.00041027070255950093 loss5 :  0.0003529548703227192\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0005063056596554816\n",
      "lossl :  0.0 loss1 :  0.00010614395432639867 loss2 :  4.558563159662299e-05 loss3 :  0.00023765563673805445\n",
      "loss4 :  5.1784514653263614e-05 loss5 :  6.513595872092992e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0005525588640011847\n",
      "lossl :  0.0 loss1 :  8.115768287098035e-05 loss2 :  7.62939453125e-05 loss3 :  0.0002347946137888357\n",
      "loss4 :  9.851455979514867e-05 loss5 :  6.179809861350805e-05\n",
      "time taken :  0.18880844116210938\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 114/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.029062125831842422\n",
      "lossl :  7.629394644936838e-07 loss1 :  0.006157398223876953 loss2 :  0.004997157957404852 loss3 :  0.009226655587553978\n",
      "loss4 :  0.005368995480239391 loss5 :  0.0033111572265625\n",
      "Iteration :  4  /  7\n",
      "loss :  0.023497581481933594\n",
      "lossl :  3.43322744811303e-06 loss1 :  0.0001413345307810232 loss2 :  0.00024776457576081157 loss3 :  0.012050151824951172\n",
      "loss4 :  0.010459423065185547 loss5 :  0.0005954742664471269\n",
      "Iteration :  7  /  7\n",
      "loss :  0.024265503510832787\n",
      "lossl :  0.0 loss1 :  4.2629242670955136e-05 loss2 :  0.007424664683640003 loss3 :  0.0046174051240086555\n",
      "loss4 :  0.010878229513764381 loss5 :  0.0013025760417804122\n",
      "time taken :  0.5802960395812988\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1168057918548584\n",
      "lossl :  0.0 loss1 :  0.007816314697265625 loss2 :  0.00011920928955078125 loss3 :  0.0012446403270587325\n",
      "loss4 :  0.10157952457666397 loss5 :  0.006046104244887829\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0026985169388353825\n",
      "lossl :  0.0 loss1 :  0.0002635955752339214 loss2 :  0.00027751922607421875 loss3 :  0.0011981010902673006\n",
      "loss4 :  8.144378807628527e-05 loss5 :  0.0008778572082519531\n",
      "Iteration :  7  /  7\n",
      "loss :  0.011194992810487747\n",
      "lossl :  0.0 loss1 :  0.0009662628290243447 loss2 :  4.882812572759576e-05 loss3 :  0.009359264746308327\n",
      "loss4 :  0.0007443428039550781 loss5 :  7.62939453125e-05\n",
      "time taken :  0.18707919120788574\n",
      "--------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 115/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0014880656963214278\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0009798526298254728 loss2 :  0.0004222869756631553 loss3 :  1.2111663636460435e-05\n",
      "loss4 :  1.754760705807712e-05 loss5 :  5.5980683100642636e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.031993817538022995\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0009598731994628906 loss2 :  0.0007694244268350303 loss3 :  0.027390003204345703\n",
      "loss4 :  0.002173089887946844 loss5 :  0.0007008552784100175\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07989811897277832\n",
      "lossl :  7.629394644936838e-07 loss1 :  0.0015557289589196444 loss2 :  0.017400121316313744 loss3 :  0.018834780901670456\n",
      "loss4 :  0.03961143642663956 loss5 :  0.002495288848876953\n",
      "time taken :  0.5837507247924805\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00184459681622684\n",
      "lossl :  0.0 loss1 :  0.00042324064997956157 loss2 :  0.00013484954251907766 loss3 :  0.0008311271667480469\n",
      "loss4 :  0.00018253325833939016 loss5 :  0.0002728462277445942\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003978824708610773\n",
      "lossl :  0.0 loss1 :  0.00020570754713844508 loss2 :  0.00027179718017578125 loss3 :  0.0004795074346475303\n",
      "loss4 :  0.0013998032081872225 loss5 :  0.0016220093239098787\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005996084306389093\n",
      "lossl :  0.0 loss1 :  0.0018308639992028475 loss2 :  0.00038433074951171875 loss3 :  0.002002668334171176\n",
      "loss4 :  0.0001964569091796875 loss5 :  0.0015817641979083419\n",
      "time taken :  0.19458913803100586\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 116/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.029183197766542435\n",
      "lossl :  4.272460864740424e-05 loss1 :  0.0010924339294433594 loss2 :  0.005086803343147039 loss3 :  0.00685009965673089\n",
      "loss4 :  0.014966154471039772 loss5 :  0.0011449813609942794\n",
      "Iteration :  4  /  7\n",
      "loss :  0.14248500764369965\n",
      "lossl :  2.4127959477482364e-05 loss1 :  0.09687085449695587 loss2 :  0.00029964448185637593 loss3 :  0.03492112085223198\n",
      "loss4 :  0.0004455566522665322 loss5 :  0.009923696517944336\n",
      "Iteration :  7  /  7\n",
      "loss :  0.26591986417770386\n",
      "lossl :  6.48498553346144e-06 loss1 :  0.00022687911405228078 loss2 :  0.005932998843491077 loss3 :  0.09277894347906113\n",
      "loss4 :  0.04287242889404297 loss5 :  0.12410211563110352\n",
      "time taken :  0.6012389659881592\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.003433513455092907\n",
      "lossl :  0.0 loss1 :  0.0009355545043945312 loss2 :  0.00028553008451126516 loss3 :  0.0008100509876385331\n",
      "loss4 :  0.00017728804959915578 loss5 :  0.0012250900035724044\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0918390303850174\n",
      "lossl :  0.0 loss1 :  0.0052702901884913445 loss2 :  0.00019483566575217992 loss3 :  0.0012349128955975175\n",
      "loss4 :  0.08037300407886505 loss5 :  0.004765987396240234\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0011285780929028988\n",
      "lossl :  0.0 loss1 :  0.0001504898100392893 loss2 :  7.43865966796875e-05 loss3 :  0.0007339477306231856\n",
      "loss4 :  9.374618821311742e-05 loss5 :  7.600784010719508e-05\n",
      "time taken :  0.178849458694458\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 117/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.050390150398015976\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.004959487821906805 loss2 :  0.008953714743256569 loss3 :  0.0007326126215048134\n",
      "loss4 :  0.014503860846161842 loss5 :  0.02124028280377388\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0004829406680073589\n",
      "lossl :  0.0 loss1 :  8.096695091808215e-05 loss2 :  8.993149094749242e-05 loss3 :  8.335113670909777e-05\n",
      "loss4 :  9.860992577159777e-05 loss5 :  0.0001300811709370464\n",
      "Iteration :  7  /  7\n",
      "loss :  1.8703513145446777\n",
      "lossl :  1.716613724056515e-06 loss1 :  0.34098029136657715 loss2 :  0.37257370352745056 loss3 :  0.5280861854553223\n",
      "loss4 :  0.4829532504081726 loss5 :  0.14575619995594025\n",
      "time taken :  0.5832948684692383\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004701090045273304\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.002141237258911133 loss2 :  9.813308861339465e-05 loss3 :  0.00057306292001158\n",
      "loss4 :  0.0001504898100392893 loss5 :  0.001737880753353238\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0012345314025878906\n",
      "lossl :  0.0 loss1 :  0.0003571510314941406 loss2 :  4.091262962901965e-05 loss3 :  0.0005400657537393272\n",
      "loss4 :  0.00016870498075149953 loss5 :  0.00012769698514603078\n",
      "Iteration :  7  /  7\n",
      "loss :  0.018683910369873047\n",
      "lossl :  0.0 loss1 :  0.0015031814109534025 loss2 :  3.4332275390625e-05 loss3 :  0.00017051697068382055\n",
      "loss4 :  0.015876103192567825 loss5 :  0.0010997771751135588\n",
      "time taken :  0.1822490692138672\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 118/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.4240627586841583\n",
      "lossl :  0.0 loss1 :  0.004088067915290594 loss2 :  0.0021463395096361637 loss3 :  0.3929600417613983\n",
      "loss4 :  0.0013812065590173006 loss5 :  0.023487091064453125\n",
      "Iteration :  4  /  7\n",
      "loss :  0.16442088782787323\n",
      "lossl :  0.0 loss1 :  0.0013528347481042147 loss2 :  0.07792949676513672 loss3 :  0.004815864376723766\n",
      "loss4 :  0.0598139762878418 loss5 :  0.020508717745542526\n",
      "Iteration :  7  /  7\n",
      "loss :  0.02003154717385769\n",
      "lossl :  0.0 loss1 :  0.0015911102527752519 loss2 :  0.0024553299881517887 loss3 :  0.01115426979959011\n",
      "loss4 :  0.0036067008040845394 loss5 :  0.0012241363292559981\n",
      "time taken :  0.5822045803070068\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.000811672187410295\n",
      "lossl :  0.0 loss1 :  9.479522850597277e-05 loss2 :  0.0001047134428517893 loss3 :  0.00038232802762649953\n",
      "loss4 :  0.0002006530703511089 loss5 :  2.918243444582913e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005483722779899836\n",
      "lossl :  0.0 loss1 :  0.00025768281193450093 loss2 :  0.0008266448858194053 loss3 :  0.0004464149533305317\n",
      "loss4 :  0.00010461806959938258 loss5 :  0.0038483620155602694\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003815364558249712\n",
      "lossl :  0.0 loss1 :  0.0006654739263467491 loss2 :  0.00025730131892487407 loss3 :  0.0006403923034667969\n",
      "loss4 :  0.002050113631412387 loss5 :  0.00020208358182571828\n",
      "time taken :  0.1819744110107422\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 119/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.29280543327331543\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.06612648814916611 loss2 :  0.0051628113724291325 loss3 :  0.14703182876110077\n",
      "loss4 :  0.05356717109680176 loss5 :  0.02091693878173828\n",
      "Iteration :  4  /  7\n",
      "loss :  0.04706253856420517\n",
      "lossl :  1.144409225162235e-06 loss1 :  0.0017406463157385588 loss2 :  0.020598793402314186 loss3 :  0.00669751176610589\n",
      "loss4 :  0.002399540040642023 loss5 :  0.01562490500509739\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5163111090660095\n",
      "lossl :  3.623962356869015e-06 loss1 :  0.2907341420650482 loss2 :  0.02104821242392063 loss3 :  0.010302496142685413\n",
      "loss4 :  0.18731975555419922 loss5 :  0.006902885623276234\n",
      "time taken :  0.596968412399292\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0005856991047039628\n",
      "lossl :  0.0 loss1 :  4.591941979015246e-05 loss2 :  4.4155120122013614e-05 loss3 :  5.5027008784236386e-05\n",
      "loss4 :  0.00013294219388626516 loss5 :  0.00030765534029342234\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03348991647362709\n",
      "lossl :  0.0 loss1 :  0.023778557777404785 loss2 :  0.00012054443504894152 loss3 :  0.0028912543784826994\n",
      "loss4 :  0.0005118370172567666 loss5 :  0.006187724880874157\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  7  /  7\n",
      "loss :  0.03966550901532173\n",
      "lossl :  0.0 loss1 :  0.0045871734619140625 loss2 :  7.905960228526965e-05 loss3 :  0.0005909919855184853\n",
      "loss4 :  0.029348183423280716 loss5 :  0.005060100462287664\n",
      "time taken :  0.18341636657714844\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 120/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06292323768138885\n",
      "lossl :  0.0 loss1 :  0.01693577691912651 loss2 :  0.002777195069938898 loss3 :  0.036235760897397995\n",
      "loss4 :  0.0036266327369958162 loss5 :  0.0033478736877441406\n",
      "Iteration :  4  /  7\n",
      "loss :  0.8251351118087769\n",
      "lossl :  0.0 loss1 :  0.0013396262656897306 loss2 :  0.0916675552725792 loss3 :  0.08691320568323135\n",
      "loss4 :  0.0027709961868822575 loss5 :  0.6424437165260315\n",
      "Iteration :  7  /  7\n",
      "loss :  1.1904308795928955\n",
      "lossl :  0.0 loss1 :  0.00024585722712799907 loss2 :  0.08407612144947052 loss3 :  0.06917957961559296\n",
      "loss4 :  0.9060417413711548 loss5 :  0.13088765740394592\n",
      "time taken :  0.5754423141479492\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00020818710618186742\n",
      "lossl :  0.0 loss1 :  8.869171324477065e-06 loss2 :  1.697540210443549e-05 loss3 :  2.994537317135837e-05\n",
      "loss4 :  0.00010757446580100805 loss5 :  4.482269287109375e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.24772222340106964\n",
      "lossl :  0.0 loss1 :  0.0013354301918298006 loss2 :  0.010941791348159313 loss3 :  0.21999768912792206\n",
      "loss4 :  0.015287017449736595 loss5 :  0.00016031265840865672\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0008050918695516884\n",
      "lossl :  0.0 loss1 :  2.288818359375e-05 loss2 :  1.602172778802924e-05 loss3 :  0.0005908965831622481\n",
      "loss4 :  0.00010242462303722277 loss5 :  7.286071922862902e-05\n",
      "time taken :  0.17542099952697754\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 121/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.08024683594703674\n",
      "lossl :  0.0 loss1 :  0.008220195770263672 loss2 :  0.0051933289505541325 loss3 :  0.012931346893310547\n",
      "loss4 :  0.030086517333984375 loss5 :  0.023815441876649857\n",
      "Iteration :  4  /  7\n",
      "loss :  0.012245654128491879\n",
      "lossl :  7.629394644936838e-07 loss1 :  0.00011157989501953125 loss2 :  0.005460166838020086 loss3 :  0.0020563125144690275\n",
      "loss4 :  0.0018335342174395919 loss5 :  0.0027832984924316406\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0058508869260549545\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.00021686553373001516 loss2 :  0.0011878013610839844 loss3 :  0.001066494034603238\n",
      "loss4 :  0.0027065277099609375 loss5 :  0.0006721496465615928\n",
      "time taken :  0.5710568428039551\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01586170308291912\n",
      "lossl :  0.0 loss1 :  0.0003456115664448589 loss2 :  0.00017366409883834422 loss3 :  0.00910110492259264\n",
      "loss4 :  0.0060860635712742805 loss5 :  0.00015525818162132055\n",
      "Iteration :  4  /  7\n",
      "loss :  0.020647907629609108\n",
      "lossl :  9.536743306171047e-08 loss1 :  3.156661841785535e-05 loss2 :  0.00013494491577148438 loss3 :  0.0041137696243822575\n",
      "loss4 :  0.016048431396484375 loss5 :  0.00031909943209029734\n",
      "Iteration :  7  /  7\n",
      "loss :  0.001895904541015625\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00012044906907249242 loss2 :  0.0006345749134197831 loss3 :  0.0003719329833984375\n",
      "loss4 :  0.0005200385930947959 loss5 :  0.0002487182500772178\n",
      "time taken :  0.1761951446533203\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 122/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004540776833891869\n",
      "lossl :  2.28881845032447e-06 loss1 :  0.00022101402282714844 loss2 :  0.0004489898565225303 loss3 :  0.0005242347833700478\n",
      "loss4 :  0.003261470701545477 loss5 :  8.277893357444555e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.041496992111206055\n",
      "lossl :  0.0 loss1 :  0.019608210772275925 loss2 :  0.0001735687255859375 loss3 :  0.01360411662608385\n",
      "loss4 :  0.0037928582169115543 loss5 :  0.0043182373046875\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010510444408282638\n",
      "lossl :  0.0 loss1 :  0.0002612113894429058 loss2 :  0.0003209114074707031 loss3 :  0.00015859604172874242\n",
      "loss4 :  2.365112231927924e-05 loss5 :  0.00028667450533248484\n",
      "time taken :  0.6096961498260498\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03823060914874077\n",
      "lossl :  0.0 loss1 :  0.009702777490019798 loss2 :  0.0006109237438067794 loss3 :  0.0015868187183514237\n",
      "loss4 :  0.004986095242202282 loss5 :  0.02134399488568306\n",
      "Iteration :  4  /  7\n",
      "loss :  0.018932629376649857\n",
      "lossl :  0.0 loss1 :  6.103515625e-05 loss2 :  0.00037889479426667094 loss3 :  0.012532425113022327\n",
      "loss4 :  0.005621910095214844 loss5 :  0.0003383636358194053\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08940286934375763\n",
      "lossl :  0.0 loss1 :  0.00012865065946243703 loss2 :  0.00039587021456100047 loss3 :  0.011309814639389515\n",
      "loss4 :  0.0764281302690506 loss5 :  0.0011404037941247225\n",
      "time taken :  0.1780402660369873\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 123/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.1316511183977127\n",
      "lossl :  0.0 loss1 :  0.0011140822898596525 loss2 :  0.07218213379383087 loss3 :  0.009412813000380993\n",
      "loss4 :  0.02948589250445366 loss5 :  0.019456196576356888\n",
      "Iteration :  4  /  7\n",
      "loss :  0.001506805419921875\n",
      "lossl :  0.0 loss1 :  0.00011205673217773438 loss2 :  5.5027008784236386e-05 loss3 :  4.472732689464465e-05\n",
      "loss4 :  0.0012736320495605469 loss5 :  2.136230432370212e-05\n",
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      "loss :  0.004389953799545765\n",
      "lossl :  9.5367431640625e-07 loss1 :  3.43322744811303e-06 loss2 :  0.00156230921857059 loss3 :  9.613037400413305e-05\n",
      "loss4 :  4.38690185546875e-05 loss5 :  0.0026832581497728825\n",
      "time taken :  0.5658588409423828\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004861736670136452\n",
      "lossl :  0.0 loss1 :  0.0004670143243856728 loss2 :  0.0014615058898925781 loss3 :  0.0010966301197186112\n",
      "loss4 :  0.0012227058177813888 loss5 :  0.0006138801691122353\n",
      "Iteration :  4  /  7\n",
      "loss :  0.11470789462327957\n",
      "lossl :  0.0 loss1 :  0.0001070022553903982 loss2 :  0.0003482818719930947 loss3 :  0.012619972229003906\n",
      "loss4 :  0.10048966109752655 loss5 :  0.0011429786682128906\n",
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      "lossl :  0.0 loss1 :  5.636215064441785e-05 loss2 :  7.534027099609375e-05 loss3 :  7.371902756858617e-05\n",
      "loss4 :  8.37326078908518e-05 loss5 :  7.81059279688634e-05\n",
      "time taken :  0.18337225914001465\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 124/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002759838243946433\n",
      "lossl :  2.8610230629055877e-07 loss1 :  4.749298022943549e-05 loss2 :  0.0012244224781170487 loss3 :  0.00039577484130859375\n",
      "loss4 :  0.00042400360689498484 loss5 :  0.0006678581121377647\n",
      "Iteration :  4  /  7\n",
      "loss :  0.023432541638612747\n",
      "lossl :  0.0 loss1 :  0.0002254486025776714 loss2 :  0.0006218909984454513 loss3 :  0.004758262541145086\n",
      "loss4 :  0.017305755987763405 loss5 :  0.0005211830139160156\n",
      "Iteration :  7  /  7\n",
      "loss :  0.16785851120948792\n",
      "lossl :  0.0 loss1 :  0.0030454634688794613 loss2 :  0.0010085105895996094 loss3 :  0.0069370269775390625\n",
      "loss4 :  0.14438000321388245 loss5 :  0.012487506493926048\n",
      "time taken :  0.5669543743133545\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.10923054069280624\n",
      "lossl :  0.0 loss1 :  0.00010604858107399195 loss2 :  0.0004100799560546875 loss3 :  0.012324142269790173\n",
      "loss4 :  0.0951363816857338 loss5 :  0.0012538910377770662\n",
      "Iteration :  4  /  7\n",
      "loss :  0.000960874545853585\n",
      "lossl :  0.0 loss1 :  0.00012230873107910156 loss2 :  0.00045518873957917094 loss3 :  0.0002938270627055317\n",
      "loss4 :  2.918243444582913e-05 loss5 :  6.0367583500919864e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0043610576540231705\n",
      "lossl :  0.0 loss1 :  0.0004387855587992817 loss2 :  8.907318260753527e-05 loss3 :  0.00045375822810456157\n",
      "loss4 :  0.003239536192268133 loss5 :  0.00013990401930641383\n",
      "time taken :  0.17661190032958984\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 125/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.20890182256698608\n",
      "lossl :  0.0 loss1 :  0.02926626242697239 loss2 :  0.10218417644500732 loss3 :  0.03895439952611923\n",
      "loss4 :  0.037752438336610794 loss5 :  0.0007445335504598916\n",
      "Iteration :  4  /  7\n",
      "loss :  0.734942615032196\n",
      "lossl :  0.0 loss1 :  0.003907585050910711 loss2 :  0.039598751813173294 loss3 :  0.09448454529047012\n",
      "loss4 :  0.005119991488754749 loss5 :  0.5918317437171936\n",
      "Iteration :  7  /  7\n",
      "loss :  0.043317265808582306\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.01609034463763237 loss2 :  0.006690788082778454 loss3 :  0.0087134363129735\n",
      "loss4 :  0.0021429061889648438 loss5 :  0.009677791967988014\n",
      "time taken :  0.563779354095459\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0013576507335528731\n",
      "lossl :  0.0 loss1 :  4.997253563487902e-05 loss2 :  0.00014371871657203883 loss3 :  0.00039968491182662547\n",
      "loss4 :  0.00011310577247058973 loss5 :  0.0006511688116006553\n",
      "Iteration :  4  /  7\n",
      "loss :  0.015789605677127838\n",
      "lossl :  0.0 loss1 :  0.00010232925706077367 loss2 :  7.114410254871473e-05 loss3 :  0.008621406741440296\n",
      "loss4 :  0.006305694580078125 loss5 :  0.0006890296936035156\n",
      "Iteration :  7  /  7\n",
      "loss :  0.29085880517959595\n",
      "lossl :  0.0 loss1 :  0.10477080196142197 loss2 :  0.0003640174982137978 loss3 :  0.04565315321087837\n",
      "loss4 :  0.03944683074951172 loss5 :  0.10062398761510849\n",
      "time taken :  0.1665935516357422\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 126/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.011521768756210804\n",
      "lossl :  0.0 loss1 :  0.0007943630334921181 loss2 :  0.0006848335033282638 loss3 :  0.0014079094398766756\n",
      "loss4 :  0.006700420286506414 loss5 :  0.0019342422019690275\n",
      "Iteration :  4  /  7\n",
      "loss :  0.05504598468542099\n",
      "lossl :  0.0 loss1 :  0.0002865791320800781 loss2 :  0.012364959344267845 loss3 :  0.0010645866859704256\n",
      "loss4 :  0.04089479520916939 loss5 :  0.00043506623478606343\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0002484321594238281\n",
      "lossl :  0.0 loss1 :  1.9073486328125e-06 loss2 :  5.33103957423009e-05 loss3 :  1.1444091796875e-05\n",
      "loss4 :  0.00015392302884720266 loss5 :  2.784729076665826e-05\n",
      "time taken :  0.5632469654083252\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00554995471611619\n",
      "lossl :  0.0 loss1 :  0.00036373137845657766 loss2 :  0.00441088667139411 loss3 :  0.00035753249539993703\n",
      "loss4 :  4.463195728021674e-05 loss5 :  0.00037317274836823344\n",
      "Iteration :  4  /  7\n",
      "loss :  5.889645099639893\n",
      "lossl :  0.0 loss1 :  0.0038962364196777344 loss2 :  1.8987693786621094 loss3 :  2.632659435272217\n",
      "loss4 :  1.3534486293792725 loss5 :  0.0008709907415322959\n",
      "Iteration :  7  /  7\n",
      "loss :  0.09637947380542755\n",
      "lossl :  0.0 loss1 :  0.004807376768440008 loss2 :  0.0007538795471191406 loss3 :  0.06375694274902344\n",
      "loss4 :  0.004316520877182484 loss5 :  0.02274475060403347\n",
      "time taken :  0.16857194900512695\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 127/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09507475048303604\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0008206367492675781 loss2 :  0.026096248999238014 loss3 :  0.014859294518828392\n",
      "loss4 :  0.04142255708575249 loss5 :  0.011875057592988014\n",
      "Iteration :  4  /  7\n",
      "loss :  0.24366635084152222\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.09632010757923126 loss2 :  0.0012045383919030428 loss3 :  0.059166621416807175\n",
      "loss4 :  0.05166683346033096 loss5 :  0.035307884216308594\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07082986831665039\n",
      "lossl :  3.814697322468419e-07 loss1 :  5.645752025884576e-05 loss2 :  0.0038941383827477694 loss3 :  0.0008097648387774825\n",
      "loss4 :  0.011392784304916859 loss5 :  0.05467634275555611\n",
      "time taken :  0.5610466003417969\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01859121397137642\n",
      "lossl :  0.0 loss1 :  4.00543212890625e-05 loss2 :  3.63349899998866e-05 loss3 :  0.0010266304016113281\n",
      "loss4 :  0.00017137527174782008 loss5 :  0.017316818237304688\n",
      "Iteration :  4  /  7\n",
      "loss :  7.157347679138184\n",
      "lossl :  0.0 loss1 :  5.998611595714465e-05 loss2 :  2.2832531929016113 loss3 :  3.0941720008850098\n",
      "loss4 :  1.7797349691390991 loss5 :  0.00012712478928733617\n",
      "Iteration :  7  /  7\n",
      "loss :  0.11409474164247513\n",
      "lossl :  0.0 loss1 :  0.0001203536958200857 loss2 :  2.079009937006049e-05 loss3 :  0.10988292843103409\n",
      "loss4 :  0.0012828826438635588 loss5 :  0.0027877807151526213\n",
      "time taken :  0.16448211669921875\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 128/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0021770477760583162\n",
      "lossl :  0.0 loss1 :  6.971359107410535e-05 loss2 :  0.001287746476009488 loss3 :  0.0004197120724711567\n",
      "loss4 :  6.170272536110133e-05 loss5 :  0.00033817291841842234\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003725147107616067\n",
      "lossl :  0.0 loss1 :  1.754760705807712e-05 loss2 :  0.00022706986055709422 loss3 :  0.0023164749145507812\n",
      "loss4 :  0.0009907722705975175 loss5 :  0.00017328262038063258\n",
      "Iteration :  7  /  7\n",
      "loss :  1.8853806257247925\n",
      "lossl :  0.0 loss1 :  1.629800796508789 loss2 :  0.0005549431079998612 loss3 :  0.11005721241235733\n",
      "loss4 :  0.005449771881103516 loss5 :  0.13951793313026428\n",
      "time taken :  0.5639879703521729\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.015501594170928001\n",
      "lossl :  0.0 loss1 :  0.00015783309936523438 loss2 :  0.0003372192441020161 loss3 :  0.01437921542674303\n",
      "loss4 :  0.0005273818969726562 loss5 :  9.994507126975805e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0010004043579101562\n",
      "lossl :  0.0 loss1 :  1.125335711549269e-05 loss2 :  6.103515625e-05 loss3 :  8.239746239269152e-05\n",
      "loss4 :  0.0007915496826171875 loss5 :  5.416870044427924e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.039057254791259766\n",
      "lossl :  0.0 loss1 :  0.0035979270469397306 loss2 :  0.00136566162109375 loss3 :  0.003056430723518133\n",
      "loss4 :  0.022740935906767845 loss5 :  0.008296298794448376\n",
      "time taken :  0.1661243438720703\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 129/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01613941229879856\n",
      "lossl :  0.0 loss1 :  0.0010085105895996094 loss2 :  0.0006244659307412803 loss3 :  0.00992288626730442\n",
      "loss4 :  0.0012939453590661287 loss5 :  0.0032896040938794613\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004673910327255726\n",
      "lossl :  0.0 loss1 :  0.0003463745233602822 loss2 :  0.0007596969371661544 loss3 :  0.002216386841610074\n",
      "loss4 :  0.0012667656410485506 loss5 :  8.468628220725805e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.09033951908349991\n",
      "lossl :  0.0 loss1 :  0.05652938038110733 loss2 :  0.0016598701477050781 loss3 :  0.00755653390660882\n",
      "loss4 :  0.0026158331893384457 loss5 :  0.021977901458740234\n",
      "time taken :  0.5585119724273682\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  2.3224151134490967\n",
      "lossl :  0.0 loss1 :  0.015276908874511719 loss2 :  0.9005481600761414 loss3 :  1.1161634922027588\n",
      "loss4 :  0.28702154755592346 loss5 :  0.003404855728149414\n",
      "Iteration :  4  /  7\n",
      "loss :  0.014341210946440697\n",
      "lossl :  0.0 loss1 :  0.00029621124849654734 loss2 :  0.0028841018211096525 loss3 :  0.003623104188591242\n",
      "loss4 :  0.00469098100438714 loss5 :  0.0028468132950365543\n",
      "Iteration :  7  /  7\n",
      "loss :  0.030008934438228607\n",
      "lossl :  0.0 loss1 :  0.011479807086288929 loss2 :  0.013298511505126953 loss3 :  0.0030028342735022306\n",
      "loss4 :  0.0005901336553506553 loss5 :  0.0016376494895666838\n",
      "time taken :  0.16644048690795898\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 130/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.03868741914629936\n",
      "lossl :  0.0 loss1 :  1.9073486328125e-06 loss2 :  0.017396926879882812 loss3 :  0.002304649446159601\n",
      "loss4 :  0.0005516052478924394 loss5 :  0.01843233034014702\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03202671930193901\n",
      "lossl :  0.0 loss1 :  5.8603287470759824e-05 loss2 :  1.049041748046875e-05 loss3 :  0.027877425774931908\n",
      "loss4 :  0.0029247284401208162 loss5 :  0.0011554717784747481\n",
      "Iteration :  7  /  7\n",
      "loss :  0.103372722864151\n",
      "lossl :  1.4591217222914565e-05 loss1 :  0.0026093958877027035 loss2 :  0.06253252178430557 loss3 :  0.0029762268532067537\n",
      "loss4 :  0.033646393567323685 loss5 :  0.0015935897827148438\n",
      "time taken :  0.5818884372711182\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09998645633459091\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.005119132809340954 loss2 :  0.004132747650146484 loss3 :  0.06848935782909393\n",
      "loss4 :  0.004512500949203968 loss5 :  0.01773214340209961\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1866692453622818\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.1682913601398468 loss2 :  0.003987693693488836 loss3 :  0.00846085511147976\n",
      "loss4 :  0.001898098038509488 loss5 :  0.004030847456306219\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5498242974281311\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0273711197078228 loss2 :  0.16742050647735596 loss3 :  0.27662795782089233\n",
      "loss4 :  0.07581605762243271 loss5 :  0.002588558243587613\n",
      "time taken :  0.2087547779083252\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 131/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04441175237298012\n",
      "lossl :  1.8119811784345075e-06 loss1 :  0.004063320346176624 loss2 :  0.002179813338443637 loss3 :  0.010796117596328259\n",
      "loss4 :  0.00937108974903822 loss5 :  0.017999600619077682\n",
      "Iteration :  4  /  7\n",
      "loss :  0.011143493466079235\n",
      "lossl :  0.0 loss1 :  0.00019636153592728078 loss2 :  0.0006742477416992188 loss3 :  0.004904461093246937\n",
      "loss4 :  0.003752803895622492 loss5 :  0.0016156196361407638\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1579439789056778\n",
      "lossl :  5.435943421616685e-06 loss1 :  0.07793812453746796 loss2 :  0.0027684210799634457 loss3 :  0.010844516567885876\n",
      "loss4 :  0.06027195602655411 loss5 :  0.006115532014518976\n",
      "time taken :  0.5910141468048096\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.12363996356725693\n",
      "lossl :  1.125335711549269e-05 loss1 :  0.053847502917051315 loss2 :  0.004931640811264515 loss3 :  0.015174007043242455\n",
      "loss4 :  0.024610042572021484 loss5 :  0.02506551705300808\n",
      "Iteration :  4  /  7\n",
      "loss :  0.10762086510658264\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.02931675873696804 loss2 :  0.013104152865707874 loss3 :  0.02386002615094185\n",
      "loss4 :  0.036278676241636276 loss5 :  0.0050601959228515625\n",
      "Iteration :  7  /  7\n",
      "loss :  0.22171272337436676\n",
      "lossl :  0.0 loss1 :  0.01835651323199272 loss2 :  0.0344180092215538 loss3 :  0.0993318110704422\n",
      "loss4 :  0.05696821212768555 loss5 :  0.012638187035918236\n",
      "time taken :  0.1825428009033203\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 132/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010293150320649147\n",
      "lossl :  2.670288040462765e-06 loss1 :  0.0033237456809729338 loss2 :  0.003855752991512418 loss3 :  0.0007941246149130166\n",
      "loss4 :  0.0006260871887207031 loss5 :  0.001690769218839705\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007630730047821999\n",
      "lossl :  1.430511474609375e-06 loss1 :  0.005110263824462891 loss2 :  0.0005180359003134072 loss3 :  0.0011101722484454513\n",
      "loss4 :  0.0005008697626180947 loss5 :  0.00038995742215774953\n",
      "Iteration :  7  /  7\n",
      "loss :  0.024053430184721947\n",
      "lossl :  0.0 loss1 :  0.0025174140464514494 loss2 :  0.01907982863485813 loss3 :  0.0003066062927246094\n",
      "loss4 :  0.00044546127901412547 loss5 :  0.0017041206592693925\n",
      "time taken :  0.5801563262939453\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.13556011021137238\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.004648017697036266 loss2 :  0.014595556072890759 loss3 :  0.02320566214621067\n",
      "loss4 :  0.007852029986679554 loss5 :  0.08525826781988144\n",
      "Iteration :  4  /  7\n",
      "loss :  0.04860720410943031\n",
      "lossl :  0.0 loss1 :  0.0002099990815622732 loss2 :  0.00094690325204283 loss3 :  0.02220172807574272\n",
      "loss4 :  0.01618490181863308 loss5 :  0.009063673205673695\n",
      "Iteration :  7  /  7\n",
      "loss :  0.06529698520898819\n",
      "lossl :  5.7220458984375e-06 loss1 :  0.018094968050718307 loss2 :  0.003930044360458851 loss3 :  0.006417179014533758\n",
      "loss4 :  0.019091416150331497 loss5 :  0.017757654190063477\n",
      "time taken :  0.19090628623962402\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 133/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.3667735159397125\n",
      "lossl :  0.0 loss1 :  0.04634132236242294 loss2 :  0.0528995506465435 loss3 :  0.2616663873195648\n",
      "loss4 :  1.125335711549269e-05 loss5 :  0.005854988005012274\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00786280632019043\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0002983093145303428 loss2 :  0.0009606361272744834 loss3 :  0.002176475478336215\n",
      "loss4 :  0.0005435943603515625 loss5 :  0.0038835047744214535\n",
      "Iteration :  7  /  7\n",
      "loss :  0.022480346262454987\n",
      "lossl :  0.0 loss1 :  0.008713245391845703 loss2 :  0.0009941101307049394 loss3 :  0.0005534171941690147\n",
      "loss4 :  0.00011491775512695312 loss5 :  0.012104654684662819\n",
      "time taken :  0.5727221965789795\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00811996404081583\n",
      "lossl :  0.0 loss1 :  0.0007356643909588456 loss2 :  0.0005492210621014237 loss3 :  0.0015106201171875\n",
      "loss4 :  0.004718780517578125 loss5 :  0.000605678535066545\n",
      "Iteration :  4  /  7\n",
      "loss :  0.019021034240722656\n",
      "lossl :  3.623962356869015e-06 loss1 :  0.01135473232716322 loss2 :  0.0006685256958007812 loss3 :  0.0016709327464923263\n",
      "loss4 :  0.003802680876106024 loss5 :  0.0015205383533611894\n",
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      "loss :  0.028754092752933502\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0007111072773113847 loss2 :  0.001764059066772461 loss3 :  0.0057961465790867805\n",
      "loss4 :  0.0017941475380212069 loss5 :  0.018688345327973366\n",
      "time taken :  0.18625831604003906\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 134/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.14018897712230682\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.005597352981567383 loss2 :  0.09736695140600204 loss3 :  0.006007194519042969\n",
      "loss4 :  0.006568431854248047 loss5 :  0.02464885637164116\n",
      "Iteration :  4  /  7\n",
      "loss :  0.02070794068276882\n",
      "lossl :  0.0 loss1 :  8.869171324477065e-06 loss2 :  0.016570329666137695 loss3 :  0.0014549255138263106\n",
      "loss4 :  0.00023183823213912547 loss5 :  0.002441978547722101\n",
      "Iteration :  7  /  7\n",
      "loss :  0.039837077260017395\n",
      "lossl :  0.0 loss1 :  0.007883310317993164 loss2 :  8.811950829112902e-05 loss3 :  0.0016594886546954513\n",
      "loss4 :  0.005239629652351141 loss5 :  0.0249665267765522\n",
      "time taken :  0.6049880981445312\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07251191139221191\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.004964017774909735 loss2 :  0.0016837120056152344 loss3 :  0.0634433776140213\n",
      "loss4 :  0.0008170127985067666 loss5 :  0.0016029358375817537\n",
      "Iteration :  4  /  7\n",
      "loss :  0.10646641254425049\n",
      "lossl :  0.0 loss1 :  0.0006030082586221397 loss2 :  0.0011075020302087069 loss3 :  0.0012631416320800781\n",
      "loss4 :  0.0035881996154785156 loss5 :  0.09990455955266953\n",
      "Iteration :  7  /  7\n",
      "loss :  0.013809919357299805\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lossl :  0.0 loss1 :  0.0005262374761514366 loss2 :  0.0012293815379962325 loss3 :  0.0016774177784100175\n",
      "loss4 :  0.0037983418442308903 loss5 :  0.006578540895134211\n",
      "time taken :  0.18609905242919922\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 135/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04993047937750816\n",
      "lossl :  0.0 loss1 :  0.0006207466358318925 loss2 :  0.00010719299461925402 loss3 :  0.0012556075816974044\n",
      "loss4 :  0.0012638091575354338 loss5 :  0.04668312147259712\n",
      "Iteration :  4  /  7\n",
      "loss :  0.060492850840091705\n",
      "lossl :  9.536743306171047e-08 loss1 :  6.933211989235133e-05 loss2 :  0.028885364532470703 loss3 :  0.0008086204761639237\n",
      "loss4 :  0.009938335046172142 loss5 :  0.020791102200746536\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006539797876030207\n",
      "lossl :  0.0006920814630575478 loss1 :  0.0019556761253625154 loss2 :  0.0015512466197833419 loss3 :  0.0007014274597167969\n",
      "loss4 :  0.000744724296964705 loss5 :  0.0008946418529376388\n",
      "time taken :  0.5736734867095947\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0004237174871377647\n",
      "lossl :  0.0 loss1 :  1.7833710444392636e-05 loss2 :  0.00014629363431595266 loss3 :  6.389617919921875e-05\n",
      "loss4 :  0.0001890182466013357 loss5 :  6.67572021484375e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.14083948731422424\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0023657798301428556 loss2 :  0.01740565337240696 loss3 :  0.009690189734101295\n",
      "loss4 :  0.037924766540527344 loss5 :  0.0734529048204422\n",
      "Iteration :  7  /  7\n",
      "loss :  0.011538123711943626\n",
      "lossl :  0.0 loss1 :  0.0001834869326557964 loss2 :  0.0006959915044717491 loss3 :  0.0010539054637774825\n",
      "loss4 :  0.00382404332049191 loss5 :  0.005780696868896484\n",
      "time taken :  0.16978788375854492\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 136/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008627796545624733\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00013227462477516383 loss2 :  0.0005407333374023438 loss3 :  0.0002418518124613911\n",
      "loss4 :  0.007605457212775946 loss5 :  0.00010728836059570312\n",
      "Iteration :  4  /  7\n",
      "loss :  0.051139071583747864\n",
      "lossl :  1.9073486612342094e-07 loss1 :  2.9277802241267636e-05 loss2 :  0.0020651817321777344 loss3 :  0.04620371013879776\n",
      "loss4 :  0.0016065597301349044 loss5 :  0.0012341499095782638\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1187439039349556\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.003954124636948109 loss2 :  0.027248192578554153 loss3 :  0.07745189964771271\n",
      "loss4 :  0.0024318695068359375 loss5 :  0.007657242007553577\n",
      "time taken :  0.5734212398529053\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010709857568144798\n",
      "lossl :  2.57492069977161e-06 loss1 :  0.005900001619011164 loss2 :  0.0007997512584552169 loss3 :  0.0014203072059899569\n",
      "loss4 :  0.0013161659007892013 loss5 :  0.0012710571754723787\n",
      "Iteration :  4  /  7\n",
      "loss :  0.12051258236169815\n",
      "lossl :  0.0 loss1 :  0.000660800957120955 loss2 :  0.0022618293296545744 loss3 :  0.0008700370672158897\n",
      "loss4 :  0.0018115043640136719 loss5 :  0.11490841209888458\n",
      "Iteration :  7  /  7\n",
      "loss :  0.016463851556181908\n",
      "lossl :  0.0 loss1 :  8.649825758766383e-05 loss2 :  0.00030117033747956157 loss3 :  0.006434488110244274\n",
      "loss4 :  0.004695224575698376 loss5 :  0.004946470260620117\n",
      "time taken :  0.16784262657165527\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 137/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008327675051987171\n",
      "lossl :  3.452301098150201e-05 loss1 :  0.00023293495178222656 loss2 :  0.006224966142326593 loss3 :  0.00023469925508834422\n",
      "loss4 :  0.0006795882945880294 loss5 :  0.0009209632989950478\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006060218904167414\n",
      "lossl :  4.57763690064894e-06 loss1 :  1.5354156857938506e-05 loss2 :  0.003919029142707586 loss3 :  0.0011584281455725431\n",
      "loss4 :  0.00021476745314430445 loss5 :  0.0007480621570721269\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007021760568022728\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.0017371177673339844 loss2 :  0.0007295608520507812 loss3 :  0.00016012191190384328\n",
      "loss4 :  0.0031828403007239103 loss5 :  0.001211452530696988\n",
      "time taken :  0.5577523708343506\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0011962890857830644\n",
      "lossl :  0.0 loss1 :  1.3256072634248994e-05 loss2 :  7.143020775401965e-05 loss3 :  4.367828296381049e-05\n",
      "loss4 :  0.00028333664522506297 loss5 :  0.0007845878717489541\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00921773910522461\n",
      "lossl :  0.0 loss1 :  0.0011009216541424394 loss2 :  0.00042238234891556203 loss3 :  0.0026018142234534025\n",
      "loss4 :  0.00280590052716434 loss5 :  0.002286720322445035\n",
      "Iteration :  7  /  7\n",
      "loss :  0.036385439336299896\n",
      "lossl :  0.0 loss1 :  0.00043659209040924907 loss2 :  0.00025882720365189016 loss3 :  0.03510856628417969\n",
      "loss4 :  0.00021200180344749242 loss5 :  0.00036945342435501516\n",
      "time taken :  0.16668701171875\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 138/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.007132291793823242\n",
      "lossl :  0.0 loss1 :  0.00034418105497024953 loss2 :  0.004790353588759899 loss3 :  0.0003781318664550781\n",
      "loss4 :  0.0002040863037109375 loss5 :  0.0014155388344079256\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1771671175956726\n",
      "lossl :  0.0 loss1 :  0.015319442376494408 loss2 :  0.004735708236694336 loss3 :  0.06900517642498016\n",
      "loss4 :  0.002554607344791293 loss5 :  0.0855521708726883\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0023567676544189453\n",
      "lossl :  0.0 loss1 :  0.0004023551882710308 loss2 :  0.0007135391351766884 loss3 :  0.0004982471582479775\n",
      "loss4 :  0.0006924628978595138 loss5 :  5.016326758777723e-05\n",
      "time taken :  0.5644340515136719\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0016405106289312243\n",
      "lossl :  0.0 loss1 :  8.306503150379285e-05 loss2 :  0.0001791000395314768 loss3 :  0.0011184692848473787\n",
      "loss4 :  0.00015478134446311742 loss5 :  0.0001050949067575857\n",
      "Iteration :  4  /  7\n",
      "loss :  0.09540458023548126\n",
      "lossl :  2.28881845032447e-06 loss1 :  0.0025197030045092106 loss2 :  0.009260368533432484 loss3 :  0.01872262917459011\n",
      "loss4 :  0.03653154522180557 loss5 :  0.028368044644594193\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009539174847304821\n",
      "lossl :  1.3351440202313825e-06 loss1 :  0.0005717277526855469 loss2 :  0.0012067795032635331 loss3 :  0.003718852996826172\n",
      "loss4 :  0.00219135289080441 loss5 :  0.0018491267692297697\n",
      "time taken :  0.16629409790039062\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 139/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.017456626519560814\n",
      "lossl :  0.0 loss1 :  0.00037479400634765625 loss2 :  0.00022325516329146922 loss3 :  0.015883350744843483\n",
      "loss4 :  0.00023813247389625758 loss5 :  0.000737094902433455\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07472028583288193\n",
      "lossl :  0.0 loss1 :  0.0004961967351846397 loss2 :  0.008180809207260609 loss3 :  0.0010954856406897306\n",
      "loss4 :  0.0002482414129190147 loss5 :  0.06469955295324326\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0023285867646336555\n",
      "lossl :  0.0 loss1 :  0.00016145706467796117 loss2 :  0.0006241798400878906 loss3 :  0.00040493012056685984\n",
      "loss4 :  0.00024023055448196828 loss5 :  0.0008977890247479081\n",
      "time taken :  0.5630433559417725\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004723453428596258\n",
      "lossl :  0.0 loss1 :  0.0009614944574423134 loss2 :  0.0007521629449911416 loss3 :  0.0002159118594136089\n",
      "loss4 :  0.00023002624220680445 loss5 :  0.002563857939094305\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010379791259765625\n",
      "lossl :  1.8119811784345075e-06 loss1 :  0.0021734237670898438 loss2 :  0.0004444122314453125 loss3 :  0.0025777816772460938\n",
      "loss4 :  0.0029078484512865543 loss5 :  0.0022745132446289062\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009817219339311123\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.00017118453979492188 loss2 :  0.0003131866396870464 loss3 :  0.007050132844597101\n",
      "loss4 :  0.00041322706965729594 loss5 :  0.0018693923484534025\n",
      "time taken :  0.16674304008483887\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 140/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  1.2891192436218262\n",
      "lossl :  0.0 loss1 :  0.002097511198371649 loss2 :  0.27993011474609375 loss3 :  0.43922585248947144\n",
      "loss4 :  0.012221908196806908 loss5 :  0.5556437373161316\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07242278754711151\n",
      "lossl :  1.8119811784345075e-06 loss1 :  6.394386582542211e-05 loss2 :  7.467270188499242e-05 loss3 :  0.027889776974916458\n",
      "loss4 :  0.02761096879839897 loss5 :  0.01678161695599556\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0033916477113962173\n",
      "lossl :  0.0 loss1 :  3.5762786865234375e-05 loss2 :  0.0031066895462572575 loss3 :  7.305145118152723e-05\n",
      "loss4 :  8.735656592762098e-05 loss5 :  8.878707740223035e-05\n",
      "time taken :  0.5588178634643555\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07234930247068405\n",
      "lossl :  0.0 loss1 :  0.06213707849383354 loss2 :  0.00012969970703125 loss3 :  0.00023756027803756297\n",
      "loss4 :  0.0013010979164391756 loss5 :  0.008543873205780983\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005960273556411266\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.0011816024780273438 loss2 :  0.00039606093196198344 loss3 :  0.0007205009460449219\n",
      "loss4 :  0.00026721955509856343 loss5 :  0.003392887068912387\n",
      "Iteration :  7  /  7\n",
      "loss :  0.001366042997688055\n",
      "lossl :  0.0 loss1 :  0.00012903213792014867 loss2 :  0.0002811431768350303 loss3 :  0.0008004188421182334\n",
      "loss4 :  6.952285912120715e-05 loss5 :  8.59260544530116e-05\n",
      "time taken :  0.1669161319732666\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 141/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.08465656638145447\n",
      "lossl :  0.0 loss1 :  0.00010442733764648438 loss2 :  0.010698032565414906 loss3 :  0.044423483312129974\n",
      "loss4 :  0.0011510848999023438 loss5 :  0.028279542922973633\n",
      "Iteration :  4  /  7\n",
      "loss :  0.010366678237915039\n",
      "lossl :  3.337860107421875e-06 loss1 :  9.72747784544481e-06 loss2 :  0.0019485473167151213 loss3 :  0.006382560823112726\n",
      "loss4 :  0.0005323410150595009 loss5 :  0.0014901638496667147\n",
      "Iteration :  7  /  7\n",
      "loss :  0.543099045753479\n",
      "lossl :  1.62124638336536e-06 loss1 :  0.003607082413509488 loss2 :  0.28373926877975464 loss3 :  0.029697131365537643\n",
      "loss4 :  0.20819321274757385 loss5 :  0.017860794439911842\n",
      "time taken :  0.565270185470581\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0035541534889489412\n",
      "lossl :  0.0 loss1 :  0.0002825736883096397 loss2 :  0.0005244255298748612 loss3 :  0.0009281158563680947\n",
      "loss4 :  0.00017786026000976562 loss5 :  0.00164117815438658\n",
      "Iteration :  4  /  7\n",
      "loss :  0.008728695102036\n",
      "lossl :  0.0 loss1 :  5.445480201160535e-05 loss2 :  0.0001180648832814768 loss3 :  0.008351325988769531\n",
      "loss4 :  0.0001010894775390625 loss5 :  0.00010375976853538305\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0035589695908129215\n",
      "lossl :  0.0 loss1 :  0.0031795979011803865 loss2 :  5.3691863286076114e-05 loss3 :  1.9168854123563506e-05\n",
      "loss4 :  0.0002498626708984375 loss5 :  5.664825584972277e-05\n",
      "time taken :  0.17710661888122559\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 142/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0009195327293127775\n",
      "lossl :  0.0 loss1 :  1.0776519957289565e-05 loss2 :  0.0004940032958984375 loss3 :  0.00014905929856467992\n",
      "loss4 :  5.626678466796875e-05 loss5 :  0.0002094268857035786\n",
      "Iteration :  4  /  7\n",
      "loss :  0.011865044012665749\n",
      "lossl :  0.0 loss1 :  0.0011172294616699219 loss2 :  0.0019376755226403475 loss3 :  0.004588508512824774\n",
      "loss4 :  0.0014553070068359375 loss5 :  0.0027663230430334806\n",
      "Iteration :  7  /  7\n",
      "loss :  0.18407727777957916\n",
      "lossl :  0.0 loss1 :  0.0030706406105309725 loss2 :  0.0024694441817700863 loss3 :  0.027529621496796608\n",
      "loss4 :  0.013453197665512562 loss5 :  0.1375543773174286\n",
      "time taken :  0.570418119430542\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03108358383178711\n",
      "lossl :  0.0 loss1 :  0.0003101348993368447 loss2 :  0.0001047134428517893 loss3 :  6.809234764659777e-05\n",
      "loss4 :  0.00210399623028934 loss5 :  0.028496647253632545\n",
      "Iteration :  4  /  7\n",
      "loss :  0.17387795448303223\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0017910003662109375 loss2 :  0.0023714066483080387 loss3 :  0.11088218539953232\n",
      "loss4 :  0.05867743492126465 loss5 :  0.00015554428682662547\n",
      "Iteration :  7  /  7\n",
      "loss :  0.008997583761811256\n",
      "lossl :  0.0 loss1 :  0.002118825912475586 loss2 :  0.0007026672246865928 loss3 :  0.0018567561637610197\n",
      "loss4 :  0.0011953354114666581 loss5 :  0.0031239986419677734\n",
      "time taken :  0.1775205135345459\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 143/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.014061450958251953\n",
      "lossl :  0.0 loss1 :  0.011734294705092907 loss2 :  0.00022563934908248484 loss3 :  0.0012840271228924394\n",
      "loss4 :  0.0007688522455282509 loss5 :  4.863739013671875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.024698257446289062\n",
      "lossl :  0.0 loss1 :  0.00016489028348587453 loss2 :  0.0001142501860158518 loss3 :  0.01525583304464817\n",
      "loss4 :  0.0004096984921488911 loss5 :  0.008753585629165173\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0018415451049804688\n",
      "lossl :  0.0 loss1 :  0.0016130447620525956 loss2 :  5.1975250244140625e-05 loss3 :  1.926422191900201e-05\n",
      "loss4 :  5.53131121705519e-06 loss5 :  0.00015172958956100047\n",
      "time taken :  0.5717177391052246\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04704399034380913\n",
      "lossl :  0.0 loss1 :  0.0003167152462992817 loss2 :  1.869201696536038e-05 loss3 :  5.7697296142578125e-05\n",
      "loss4 :  0.0033834457863122225 loss5 :  0.04326744005084038\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0029953003395348787\n",
      "lossl :  0.0 loss1 :  0.0007862091297283769 loss2 :  0.0004672050417866558 loss3 :  0.00028734205989167094\n",
      "loss4 :  0.000935459160245955 loss5 :  0.0005190849187783897\n",
      "Iteration :  7  /  7\n",
      "loss :  0.002509975340217352\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0014066696166992188 loss2 :  0.00014276504225563258 loss3 :  0.0003012657107319683\n",
      "loss4 :  0.0004907607799395919 loss5 :  0.0001682281435932964\n",
      "time taken :  0.17947626113891602\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 144/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.6207976937294006\n",
      "lossl :  0.0 loss1 :  4.7016143071232364e-05 loss2 :  0.23047542572021484 loss3 :  0.00013217926607467234\n",
      "loss4 :  0.27186059951782227 loss5 :  0.1182825118303299\n",
      "Iteration :  4  /  7\n",
      "loss :  0.001621246337890625\n",
      "lossl :  3.814697322468419e-07 loss1 :  1.62124638336536e-06 loss2 :  0.0006625175592489541 loss3 :  0.00014772414579056203\n",
      "loss4 :  0.0002597808779682964 loss5 :  0.0005492210621014237\n",
      "Iteration :  7  /  7\n",
      "loss :  0.07849347591400146\n",
      "lossl :  0.0 loss1 :  0.07133372128009796 loss2 :  0.0037391663063317537 loss3 :  0.00038042067899368703\n",
      "loss4 :  0.0027722835075110197 loss5 :  0.00026788710965774953\n",
      "time taken :  0.5708260536193848\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0015738486545160413\n",
      "lossl :  0.0 loss1 :  2.1457672119140625e-05 loss2 :  0.0009360313415527344 loss3 :  0.00024471283541060984\n",
      "loss4 :  0.00017280578322242945 loss5 :  0.00019884109497070312\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0013524055248126388\n",
      "lossl :  0.0 loss1 :  0.00013999939255882055 loss2 :  0.00020656586275435984 loss3 :  0.00047931671724654734\n",
      "loss4 :  0.00011405944678699598 loss5 :  0.0004124641418457031\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0021055222023278475\n",
      "lossl :  0.0 loss1 :  0.0006080627208575606 loss2 :  0.0001548767031636089 loss3 :  0.0006386757013387978\n",
      "loss4 :  0.0003130912664346397 loss5 :  0.00039081572322174907\n",
      "time taken :  0.17752742767333984\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 145/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.15498466789722443\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.00735893240198493 loss2 :  0.14568166434764862 loss3 :  0.0007928848499432206\n",
      "loss4 :  0.0010633468627929688 loss5 :  8.716583397472277e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.06527233868837357\n",
      "lossl :  0.0 loss1 :  0.0005339622730389237 loss2 :  0.06195125728845596 loss3 :  0.0002777099725790322\n",
      "loss4 :  0.0008030891185626388 loss5 :  0.0017063140403479338\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005085277836769819\n",
      "lossl :  0.0 loss1 :  0.0008826255798339844 loss2 :  0.0018171310657635331 loss3 :  0.0018701553344726562\n",
      "loss4 :  0.0004913330194540322 loss5 :  2.403259350103326e-05\n",
      "time taken :  0.5638525485992432\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2125474065542221\n",
      "lossl :  0.0 loss1 :  0.0006570816040039062 loss2 :  0.005065536592155695 loss3 :  0.18363896012306213\n",
      "loss4 :  0.02287311479449272 loss5 :  0.0003127098025288433\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0029132841154932976\n",
      "lossl :  0.0 loss1 :  4.653930591302924e-05 loss2 :  0.0023325919173657894 loss3 :  0.00019760131544899195\n",
      "loss4 :  0.0002079010009765625 loss5 :  0.00012865065946243703\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0009005547035485506\n",
      "lossl :  0.0 loss1 :  4.9114227294921875e-05 loss2 :  0.000102996826171875 loss3 :  8.544921729480848e-05\n",
      "loss4 :  0.0006502151372842491 loss5 :  1.277923547604587e-05\n",
      "time taken :  0.1806774139404297\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 146/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0025557519402354956\n",
      "lossl :  1.9073486612342094e-07 loss1 :  1.869201696536038e-05 loss2 :  0.0002609252987895161 loss3 :  0.00113086705096066\n",
      "loss4 :  0.0009529113885946572 loss5 :  0.00019216537475585938\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0036779879592359066\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00028548241243697703 loss2 :  0.0001300811709370464 loss3 :  0.0016917228931561112\n",
      "loss4 :  0.0004627227899618447 loss5 :  0.0011077880626544356\n",
      "Iteration :  7  /  7\n",
      "loss :  0.25166717171669006\n",
      "lossl :  0.0 loss1 :  0.011298870667815208 loss2 :  0.2132844477891922 loss3 :  0.0032110214233398438\n",
      "loss4 :  0.002208805177360773 loss5 :  0.021664047613739967\n",
      "time taken :  0.5708789825439453\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00032978056697174907\n",
      "lossl :  0.0 loss1 :  2.250671423098538e-05 loss2 :  4.863739013671875e-05 loss3 :  0.00019950866408180445\n",
      "loss4 :  2.651214526849799e-05 loss5 :  3.261566234868951e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0009377480018883944\n",
      "lossl :  0.0 loss1 :  2.6798248654813506e-05 loss2 :  0.0001873016299214214 loss3 :  0.000331878662109375\n",
      "loss4 :  0.00033321380033157766 loss5 :  5.855560448253527e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.061121463775634766\n",
      "lossl :  0.0 loss1 :  0.00030498503474518657 loss2 :  0.007793235592544079 loss3 :  0.00028676987858489156\n",
      "loss4 :  0.006260871887207031 loss5 :  0.046475600451231\n",
      "time taken :  0.18566513061523438\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 147/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09208526462316513\n",
      "lossl :  1.9073486612342094e-07 loss1 :  6.28471389063634e-05 loss2 :  0.07588963210582733 loss3 :  0.008170795626938343\n",
      "loss4 :  0.006689119152724743 loss5 :  0.0012726783752441406\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07119659334421158\n",
      "lossl :  0.0 loss1 :  0.0002769470156636089 loss2 :  0.03461947292089462 loss3 :  0.031984616070985794\n",
      "loss4 :  0.0022967339027673006 loss5 :  0.002018833067268133\n",
      "Iteration :  7  /  7\n",
      "loss :  0.058167167007923126\n",
      "lossl :  0.0 loss1 :  4.100799742445815e-06 loss2 :  0.04105949401855469 loss3 :  0.0009373665088787675\n",
      "loss4 :  0.01606759987771511 loss5 :  9.860992577159777e-05\n",
      "time taken :  0.5704827308654785\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002447414444759488\n",
      "lossl :  0.0 loss1 :  4.0531158447265625e-05 loss2 :  0.0003169059637002647 loss3 :  0.00013103484525345266\n",
      "loss4 :  0.0017002106178551912 loss5 :  0.00025873183039948344\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00027523041353560984\n",
      "lossl :  0.0 loss1 :  3.347396705066785e-05 loss2 :  0.0001354217529296875 loss3 :  5.187988426769152e-05\n",
      "loss4 :  2.174377368646674e-05 loss5 :  3.2711028325138614e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.010382270440459251\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00014238357834983617 loss2 :  0.0027963637840002775 loss3 :  0.00030727387638762593\n",
      "loss4 :  0.004963016603142023 loss5 :  0.0021730423904955387\n",
      "time taken :  0.1938462257385254\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 148/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04816832393407822\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.006048440933227539 loss2 :  0.0008376121404580772 loss3 :  0.01985640451312065\n",
      "loss4 :  0.020863676443696022 loss5 :  0.000561618828214705\n",
      "Iteration :  4  /  7\n",
      "loss :  0.006321573164314032\n",
      "lossl :  0.0 loss1 :  0.005060434341430664 loss2 :  1.3351440202313825e-06 loss3 :  0.00034046173095703125\n",
      "loss4 :  1.9073486612342094e-07 loss5 :  0.0009191512945108116\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1259440928697586\n",
      "lossl :  0.0 loss1 :  0.00012540817260742188 loss2 :  0.0656186118721962 loss3 :  0.01926565170288086\n",
      "loss4 :  0.01592388190329075 loss5 :  0.02501053735613823\n",
      "time taken :  0.5868029594421387\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05032148212194443\n",
      "lossl :  0.0 loss1 :  0.0023452758323401213 loss2 :  2.231597864010837e-05 loss3 :  0.0014083862770348787\n",
      "loss4 :  0.005303478334099054 loss5 :  0.041242025792598724\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0002896309015341103\n",
      "lossl :  0.0 loss1 :  5.035400317865424e-05 loss2 :  3.108978125965223e-05 loss3 :  6.532669067382812e-05\n",
      "loss4 :  8.277893357444555e-05 loss5 :  6.008148193359375e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0029744149651378393\n",
      "lossl :  0.0 loss1 :  0.0021730423904955387 loss2 :  0.00048322678776457906 loss3 :  0.00010385513451183215\n",
      "loss4 :  7.114410254871473e-05 loss5 :  0.00014314652071334422\n",
      "time taken :  0.18890595436096191\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 149/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010829639621078968\n",
      "lossl :  4.19616708313697e-06 loss1 :  0.0006427764892578125 loss2 :  0.0018677711486816406 loss3 :  0.001957416534423828\n",
      "loss4 :  0.005085945129394531 loss5 :  0.0012715340126305819\n",
      "Iteration :  4  /  7\n",
      "loss :  0.05482735484838486\n",
      "lossl :  0.0 loss1 :  0.05142216756939888 loss2 :  0.000244140625 loss3 :  0.0018061638111248612\n",
      "loss4 :  0.0013445854419842362 loss5 :  1.029968279908644e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.015140055678784847\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.010046577081084251 loss2 :  0.0007358550792559981 loss3 :  0.0031538009643554688\n",
      "loss4 :  0.00034437180147506297 loss5 :  0.0008592605590820312\n",
      "time taken :  0.5801413059234619\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.72026526927948\n",
      "lossl :  0.0 loss1 :  0.003984355833381414 loss2 :  0.06613016128540039 loss3 :  0.5244187116622925\n",
      "loss4 :  0.12439250946044922 loss5 :  0.0013395309215411544\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0079049589112401\n",
      "lossl :  0.0 loss1 :  0.00047702790470793843 loss2 :  0.003325986908748746 loss3 :  0.0010715484386309981\n",
      "loss4 :  0.0020079612731933594 loss5 :  0.0010224342113360763\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0031364441383630037\n",
      "lossl :  0.0 loss1 :  0.00019321442232467234 loss2 :  0.00017871856107376516 loss3 :  0.0021848678588867188\n",
      "loss4 :  0.0004420280456542969 loss5 :  0.00013761520676780492\n",
      "time taken :  0.19015812873840332\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 150/199\n",
      "--------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008451605215668678\n",
      "lossl :  0.0 loss1 :  0.0002884864807128906 loss2 :  0.001903533935546875 loss3 :  0.0015934944385662675\n",
      "loss4 :  0.004173994064331055 loss5 :  0.000492095947265625\n",
      "Iteration :  4  /  7\n",
      "loss :  0.09832756221294403\n",
      "lossl :  0.0 loss1 :  0.03611910343170166 loss2 :  0.003504085587337613 loss3 :  0.0526391975581646\n",
      "loss4 :  0.005368518643081188 loss5 :  0.0006966590881347656\n",
      "Iteration :  7  /  7\n",
      "loss :  0.8910096287727356\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.006966924760490656 loss2 :  0.09790220111608505 loss3 :  0.7463115453720093\n",
      "loss4 :  0.03362102434039116 loss5 :  0.006207275204360485\n",
      "time taken :  0.5778398513793945\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0052311900071799755\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.004158687777817249 loss2 :  0.00014715194993186742 loss3 :  0.00043592453585006297\n",
      "loss4 :  0.00012969970703125 loss5 :  0.00035953521728515625\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1825677901506424\n",
      "lossl :  8.583068620282575e-07 loss1 :  0.0006436348194256425 loss2 :  0.0010514259338378906 loss3 :  0.0006268501165322959\n",
      "loss4 :  0.014267826452851295 loss5 :  0.16597719490528107\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08182521909475327\n",
      "lossl :  0.0 loss1 :  0.07458014786243439 loss2 :  0.00020990372286178172 loss3 :  0.005337238311767578\n",
      "loss4 :  0.0010845183860510588 loss5 :  0.0006134033319540322\n",
      "time taken :  0.18713831901550293\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 151/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.025779152289032936\n",
      "lossl :  0.0 loss1 :  6.237030174816027e-05 loss2 :  0.02377929724752903 loss3 :  0.0004295349062886089\n",
      "loss4 :  0.0007162094116210938 loss5 :  0.0007917404291220009\n",
      "Iteration :  4  /  7\n",
      "loss :  0.1229778528213501\n",
      "lossl :  1.144409225162235e-06 loss1 :  0.0008335113525390625 loss2 :  0.027054786682128906 loss3 :  0.012904023751616478\n",
      "loss4 :  0.052630018442869186 loss5 :  0.029554367065429688\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5805427432060242\n",
      "lossl :  1.23977656585339e-06 loss1 :  0.03752892091870308 loss2 :  0.10161719471216202 loss3 :  0.01410741824656725\n",
      "loss4 :  0.0037528991233557463 loss5 :  0.4235350489616394\n",
      "time taken :  0.5883700847625732\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03944521024823189\n",
      "lossl :  0.0 loss1 :  0.0292816162109375 loss2 :  0.007318687625229359 loss3 :  0.0005736351013183594\n",
      "loss4 :  0.0017631531227380037 loss5 :  0.0005081176641397178\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007595825474709272\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0047607421875 loss2 :  0.0006031990051269531 loss3 :  0.0012889861827716231\n",
      "loss4 :  0.0006539344904012978 loss5 :  0.0002880096435546875\n",
      "Iteration :  7  /  7\n",
      "loss :  0.4806163012981415\n",
      "lossl :  0.0 loss1 :  0.010263299569487572 loss2 :  0.025257205590605736 loss3 :  0.31736883521080017\n",
      "loss4 :  0.12766079604625702 loss5 :  6.618499901378527e-05\n",
      "time taken :  0.1806626319885254\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 152/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01849336549639702\n",
      "lossl :  0.0 loss1 :  0.011216687969863415 loss2 :  0.0018163680797442794 loss3 :  0.0018012046348303556\n",
      "loss4 :  0.0007173538324423134 loss5 :  0.0029417513869702816\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00737919844686985\n",
      "lossl :  0.0 loss1 :  1.029968279908644e-05 loss2 :  0.0033126354683190584 loss3 :  0.00035228728665970266\n",
      "loss4 :  0.00033254624577239156 loss5 :  0.0033714293967932463\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0017125130398198962\n",
      "lossl :  0.0 loss1 :  1.964569128176663e-05 loss2 :  5.321502612787299e-05 loss3 :  0.0009707451099529862\n",
      "loss4 :  0.00021629333787132055 loss5 :  0.00045261383638717234\n",
      "time taken :  0.5732083320617676\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.010731220245361328\n",
      "lossl :  0.0 loss1 :  0.009661388583481312 loss2 :  9.841918654274195e-05 loss3 :  0.0005975723033770919\n",
      "loss4 :  0.0002418518124613911 loss5 :  0.0001319885195698589\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0519254207611084\n",
      "lossl :  1.430511474609375e-06 loss1 :  0.012311029247939587 loss2 :  0.0005867004510946572 loss3 :  0.0004531860467977822\n",
      "loss4 :  0.002616024110466242 loss5 :  0.03595704957842827\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0028570175636559725\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0006284713745117188 loss2 :  0.0005075454828329384 loss3 :  0.000552272773347795\n",
      "loss4 :  0.0005770683055743575 loss5 :  0.0005915641668252647\n",
      "time taken :  0.17994022369384766\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 153/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.03150281682610512\n",
      "lossl :  1.0490417707842425e-06 loss1 :  0.030140399932861328 loss2 :  0.0005684852367267013 loss3 :  2.212524486822076e-05\n",
      "loss4 :  0.0001468658447265625 loss5 :  0.0006238937494345009\n",
      "Iteration :  4  /  7\n",
      "loss :  0.692435622215271\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.2012363225221634 loss2 :  0.001073551131412387 loss3 :  0.0999290719628334\n",
      "loss4 :  0.00016183852858375758 loss5 :  0.3900347352027893\n",
      "Iteration :  7  /  7\n",
      "loss :  0.012056590057909489\n",
      "lossl :  0.0 loss1 :  0.004697752185165882 loss2 :  2.307891918462701e-05 loss3 :  0.0010213851928710938\n",
      "loss4 :  0.005695057101547718 loss5 :  0.0006193161243572831\n",
      "time taken :  0.572059154510498\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.016814280301332474\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0011229992378503084 loss2 :  0.010203266516327858 loss3 :  0.0005167961353436112\n",
      "loss4 :  0.004033088684082031 loss5 :  0.0009379386901855469\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004318285267800093\n",
      "lossl :  0.0 loss1 :  0.0035071850288659334 loss2 :  9.994507126975805e-05 loss3 :  0.00045843125553801656\n",
      "loss4 :  0.00020294189744163305 loss5 :  4.978180004400201e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.014778471551835537\n",
      "lossl :  1.9073486328125e-06 loss1 :  0.013463640585541725 loss2 :  0.0002197265566792339 loss3 :  0.00019187926955055445\n",
      "loss4 :  0.00038242340087890625 loss5 :  0.0005188941722735763\n",
      "time taken :  0.18309879302978516\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 154/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002680349163711071\n",
      "lossl :  0.0 loss1 :  0.00019497871107887477 loss2 :  0.002072238828986883 loss3 :  3.166198803228326e-05\n",
      "loss4 :  0.00037593842716887593 loss5 :  5.53131121705519e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007447337731719017\n",
      "lossl :  5.722046125811175e-07 loss1 :  4.272460864740424e-05 loss2 :  0.0059455870650708675 loss3 :  0.0012354850769042969\n",
      "loss4 :  7.22885160939768e-05 loss5 :  0.0001506805419921875\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003919744398444891\n",
      "lossl :  0.0 loss1 :  0.0006345271831378341 loss2 :  0.0012162209022790194 loss3 :  0.0019584656693041325\n",
      "loss4 :  3.06129441014491e-05 loss5 :  7.99179106252268e-05\n",
      "time taken :  0.5714685916900635\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.06425780802965164\n",
      "lossl :  0.0 loss1 :  0.04822516441345215 loss2 :  0.0006260871887207031 loss3 :  0.0060708047822117805\n",
      "loss4 :  0.008874845691025257 loss5 :  0.00046091078547760844\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03641514852643013\n",
      "lossl :  0.0 loss1 :  0.00040454865666106343 loss2 :  0.00030012131901457906 loss3 :  0.0021872520446777344\n",
      "loss4 :  0.033089686185121536 loss5 :  0.00043354035005904734\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05787353217601776\n",
      "lossl :  0.0 loss1 :  0.00026226043701171875 loss2 :  0.0002620696905069053 loss3 :  0.0005130767822265625\n",
      "loss4 :  0.0027295113541185856 loss5 :  0.054106615483760834\n",
      "time taken :  0.18911123275756836\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 155/199\n",
      "--------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07550907135009766\n",
      "lossl :  0.0 loss1 :  0.00010805130295921117 loss2 :  2.632141149661038e-05 loss3 :  3.223419116693549e-05\n",
      "loss4 :  0.07460260391235352 loss5 :  0.0007398605230264366\n",
      "Iteration :  4  /  7\n",
      "loss :  8.125304884742945e-05\n",
      "lossl :  0.0 loss1 :  4.291534423828125e-05 loss2 :  2.689361645025201e-05 loss3 :  5.14984139954322e-06\n",
      "loss4 :  2.28881845032447e-06 loss5 :  4.00543194700731e-06\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004320716951042414\n",
      "lossl :  0.0 loss1 :  2.117157055181451e-05 loss2 :  0.0010227203601971269 loss3 :  0.0002991676446981728\n",
      "loss4 :  0.0025260925758630037 loss5 :  0.0004515647888183594\n",
      "time taken :  0.5746934413909912\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01086125336587429\n",
      "lossl :  0.0 loss1 :  0.001793336821720004 loss2 :  0.007446670439094305 loss3 :  0.00017938614473678172\n",
      "loss4 :  0.0005898475646972656 loss5 :  0.0008520126575604081\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0016840933822095394\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0001964569091796875 loss2 :  0.0005205154302529991 loss3 :  0.00045518873957917094\n",
      "loss4 :  0.0001947402924997732 loss5 :  0.00031681061955168843\n",
      "Iteration :  7  /  7\n",
      "loss :  0.15186628699302673\n",
      "lossl :  0.0 loss1 :  0.00026988983154296875 loss2 :  0.00010948181443382055 loss3 :  0.006580638699233532\n",
      "loss4 :  0.03889594227075577 loss5 :  0.10601034015417099\n",
      "time taken :  0.17977380752563477\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 156/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0180022232234478\n",
      "lossl :  0.0 loss1 :  0.0006588936084881425 loss2 :  0.0033037185203284025 loss3 :  0.0013822555774822831\n",
      "loss4 :  0.0035659789573401213 loss5 :  0.009091377258300781\n",
      "Iteration :  4  /  7\n",
      "loss :  0.008745193481445312\n",
      "lossl :  0.0 loss1 :  7.05718994140625e-05 loss2 :  0.0039498331025242805 loss3 :  0.0008332252618856728\n",
      "loss4 :  0.0002227783261332661 loss5 :  0.0036687850952148438\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003082370851188898\n",
      "lossl :  0.0 loss1 :  2.880096508306451e-05 loss2 :  3.337860107421875e-05 loss3 :  0.0026750564575195312\n",
      "loss4 :  0.0003338813839945942 loss5 :  1.125335711549269e-05\n",
      "time taken :  0.5725913047790527\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.539838433265686\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0009168625110760331 loss2 :  0.24005135893821716 loss3 :  0.6812203526496887\n",
      "loss4 :  0.6171194314956665 loss5 :  0.0005303382640704513\n",
      "Iteration :  4  /  7\n",
      "loss :  0.02516307681798935\n",
      "lossl :  0.0 loss1 :  0.017958927899599075 loss2 :  0.0005576133844442666 loss3 :  0.0015418052207678556\n",
      "loss4 :  0.0034471512772142887 loss5 :  0.0016575813060626388\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007707309443503618\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0009018898126669228 loss2 :  0.00040645600529387593 loss3 :  0.0051024435088038445\n",
      "loss4 :  0.0004652976931538433 loss5 :  0.0008302688365802169\n",
      "time taken :  0.18451619148254395\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 157/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.003409099532291293\n",
      "lossl :  0.0 loss1 :  1.4019012269272935e-05 loss2 :  0.00028133392333984375 loss3 :  0.0016603469848632812\n",
      "loss4 :  7.07626313669607e-05 loss5 :  0.00138263707049191\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0035840990021824837\n",
      "lossl :  9.536743306171047e-08 loss1 :  3.0231476557673886e-05 loss2 :  0.0007888794061727822 loss3 :  0.0019336700206622481\n",
      "loss4 :  0.0005672454717569053 loss5 :  0.0002639770391397178\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0004771232488565147\n",
      "lossl :  0.0 loss1 :  0.0002690315304789692 loss2 :  9.93728608591482e-05 loss3 :  2.822876012942288e-05\n",
      "loss4 :  8.01086389401462e-06 loss5 :  7.2479248046875e-05\n",
      "time taken :  0.579094648361206\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00359344482421875\n",
      "lossl :  0.0 loss1 :  7.743835158180445e-05 loss2 :  7.25746140233241e-05 loss3 :  0.003208637237548828\n",
      "loss4 :  0.0001453399599995464 loss5 :  8.94546537892893e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.027463579550385475\n",
      "lossl :  0.0 loss1 :  0.0001848220854299143 loss2 :  0.0002858162042684853 loss3 :  0.0017141342395916581\n",
      "loss4 :  0.024750947952270508 loss5 :  0.0005278587341308594\n",
      "Iteration :  7  /  7\n",
      "loss :  0.09928102046251297\n",
      "lossl :  0.0 loss1 :  0.0006583213689737022 loss2 :  0.0003273010370321572 loss3 :  0.0009617805480957031\n",
      "loss4 :  0.006038093473762274 loss5 :  0.091295525431633\n",
      "time taken :  0.1889960765838623\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 158/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00989689864218235\n",
      "lossl :  1.621246337890625e-05 loss1 :  0.0018613338470458984 loss2 :  0.0003955840948037803 loss3 :  0.006995105650275946\n",
      "loss4 :  0.00020914078049827367 loss5 :  0.0004195213259663433\n",
      "Iteration :  4  /  7\n",
      "loss :  0.028257941827178\n",
      "lossl :  2.956390289909905e-06 loss1 :  0.026307201012969017 loss2 :  0.00019521712965797633 loss3 :  0.00043973923311568797\n",
      "loss4 :  0.00019865036301780492 loss5 :  0.0011141777504235506\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0013285636669024825\n",
      "lossl :  0.0 loss1 :  8.96453821042087e-06 loss2 :  0.0005741119384765625 loss3 :  9.269714064430445e-05\n",
      "loss4 :  0.0005928039317950606 loss5 :  5.998611595714465e-05\n",
      "time taken :  0.5749015808105469\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004804467782378197\n",
      "lossl :  0.0 loss1 :  0.001960229827091098 loss2 :  0.00046367646427825093 loss3 :  0.0008031845209188759\n",
      "loss4 :  0.0007483482477255166 loss5 :  0.0008290290716104209\n",
      "Iteration :  4  /  7\n",
      "loss :  0.12439526617527008\n",
      "lossl :  0.0 loss1 :  0.02857990190386772 loss2 :  0.000102996826171875 loss3 :  0.009213542565703392\n",
      "loss4 :  0.00709609966725111 loss5 :  0.07940272986888885\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006153964903205633\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0006547927623614669 loss2 :  0.0008183479076251388 loss3 :  0.003059482667595148\n",
      "loss4 :  0.0005661010509356856 loss5 :  0.0010542869567871094\n",
      "time taken :  0.1868298053741455\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 159/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0043190959841012955\n",
      "lossl :  0.0 loss1 :  7.06672653905116e-05 loss2 :  0.0003925323544535786 loss3 :  0.003730201628059149\n",
      "loss4 :  4.215240551275201e-05 loss5 :  8.354186866199598e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004151248838752508\n",
      "lossl :  0.0 loss1 :  1.33514404296875e-05 loss2 :  0.000986385392025113 loss3 :  0.00012664795212913305\n",
      "loss4 :  0.0006879806751385331 loss5 :  0.0023368834517896175\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00174036028329283\n",
      "lossl :  0.0 loss1 :  9.260177466785535e-05 loss2 :  0.00022182465181685984 loss3 :  0.0002311706484761089\n",
      "loss4 :  0.00020160674466751516 loss5 :  0.0009931564563885331\n",
      "time taken :  0.5860805511474609\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004406070802360773\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0010485649108886719 loss2 :  0.0004902839427813888 loss3 :  0.0020324706565588713\n",
      "loss4 :  0.00015506744966842234 loss5 :  0.0006793976062908769\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0020686150528490543\n",
      "lossl :  3.814697322468419e-07 loss1 :  5.3691863286076114e-05 loss2 :  0.00048322678776457906 loss3 :  0.00027179718017578125\n",
      "loss4 :  0.0010140419472008944 loss5 :  0.00024547576322220266\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  7  /  7\n",
      "loss :  0.0013850212562829256\n",
      "lossl :  0.0 loss1 :  0.00026617050752975047 loss2 :  0.0002344131498830393 loss3 :  0.0002134323149221018\n",
      "loss4 :  0.0004287719784770161 loss5 :  0.0002422332763671875\n",
      "time taken :  0.19713330268859863\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 160/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2986500859260559\n",
      "lossl :  0.0 loss1 :  0.030226994305849075 loss2 :  0.04452161863446236 loss3 :  0.10729608684778214\n",
      "loss4 :  0.08347473293542862 loss5 :  0.033130645751953125\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00564308138564229\n",
      "lossl :  3.814697322468419e-07 loss1 :  5.14984139954322e-06 loss2 :  0.005545902065932751 loss3 :  8.153915405273438e-05\n",
      "loss4 :  7.05719003235572e-06 loss5 :  3.051757857974735e-06\n",
      "Iteration :  7  /  7\n",
      "loss :  0.020892810076475143\n",
      "lossl :  0.0 loss1 :  4.9877166020451114e-05 loss2 :  4.158019874012098e-05 loss3 :  0.002837562467902899\n",
      "loss4 :  0.0003277778741903603 loss5 :  0.0176360122859478\n",
      "time taken :  0.5758512020111084\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004816150758415461\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0002532005310058594 loss2 :  0.002788448240607977 loss3 :  0.00019598007202148438\n",
      "loss4 :  0.0009634018060751259 loss5 :  0.000615024589933455\n",
      "Iteration :  4  /  7\n",
      "loss :  0.03027486801147461\n",
      "lossl :  0.0 loss1 :  0.01734027825295925 loss2 :  0.00017070770263671875 loss3 :  0.007237338926643133\n",
      "loss4 :  0.005149936769157648 loss5 :  0.00037660598172806203\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005475902464240789\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0004904746892862022 loss2 :  0.0008224487537518144 loss3 :  0.0026527405716478825\n",
      "loss4 :  0.0002690315304789692 loss5 :  0.0012402534484863281\n",
      "time taken :  0.18608450889587402\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 161/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00081043248064816\n",
      "lossl :  3.43322744811303e-06 loss1 :  9.632110959501006e-06 loss2 :  0.00017223358736373484 loss3 :  5.245208740234375e-05\n",
      "loss4 :  0.00021629333787132055 loss5 :  0.0003563881036825478\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0004197120724711567\n",
      "lossl :  0.0 loss1 :  5.52177443751134e-05 loss2 :  8.94546537892893e-05 loss3 :  0.00022201538376975805\n",
      "loss4 :  5.722046125811175e-07 loss5 :  5.245208740234375e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.06040911749005318\n",
      "lossl :  0.0 loss1 :  0.001018571900203824 loss2 :  0.0025692940689623356 loss3 :  0.006540489383041859\n",
      "loss4 :  0.04181671142578125 loss5 :  0.008464050479233265\n",
      "time taken :  0.5874433517456055\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  1.0298413038253784\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.0004824638308491558 loss2 :  0.12412681430578232 loss3 :  0.5838093757629395\n",
      "loss4 :  0.3207017481327057 loss5 :  0.0007203101995401084\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0032227993942797184\n",
      "lossl :  0.0 loss1 :  0.002123212907463312 loss2 :  0.00022935867309570312 loss3 :  0.00015964507474564016\n",
      "loss4 :  0.0005316734313964844 loss5 :  0.0001789093075785786\n",
      "Iteration :  7  /  7\n",
      "loss :  0.029737090691924095\n",
      "lossl :  0.0 loss1 :  0.014290141873061657 loss2 :  0.00011940002150367945 loss3 :  0.01091833133250475\n",
      "loss4 :  0.004102230072021484 loss5 :  0.0003069877566304058\n",
      "time taken :  0.19185209274291992\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 162/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0037425996270030737\n",
      "lossl :  0.0 loss1 :  0.0008715629810467362 loss2 :  9.441375732421875e-05 loss3 :  0.0019615173805505037\n",
      "loss4 :  0.0004672050417866558 loss5 :  0.0003479003789834678\n",
      "Iteration :  4  /  7\n",
      "loss :  0.046074770390987396\n",
      "lossl :  0.0 loss1 :  4.367828296381049e-05 loss2 :  0.04381303861737251 loss3 :  0.0003031730593647808\n",
      "loss4 :  0.00035572052001953125 loss5 :  0.0015591621631756425\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05733218044042587\n",
      "lossl :  0.0 loss1 :  1.049041748046875e-05 loss2 :  0.007169055752456188 loss3 :  0.017447710037231445\n",
      "loss4 :  0.0021017552353441715 loss5 :  0.03060317039489746\n",
      "time taken :  0.5849926471710205\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.033377744257450104\n",
      "lossl :  5.722046125811175e-07 loss1 :  3.80516066798009e-05 loss2 :  0.0006263732793740928 loss3 :  0.0001985549897653982\n",
      "loss4 :  0.0030646324157714844 loss5 :  0.02944955788552761\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007672023959457874\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.0017939567333087325 loss2 :  0.0009372711065225303 loss3 :  0.002398872282356024\n",
      "loss4 :  0.0011636733543127775 loss5 :  0.0013777732383459806\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0006075859419070184\n",
      "lossl :  0.0 loss1 :  9.803772263694555e-05 loss2 :  0.0001199722319142893 loss3 :  0.0002418518124613911\n",
      "loss4 :  7.505416579078883e-05 loss5 :  7.26699799997732e-05\n",
      "time taken :  0.19582509994506836\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 163/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.001291465712711215\n",
      "lossl :  0.0 loss1 :  6.008148375258315e-06 loss2 :  0.00021495818509720266 loss3 :  9.269714064430445e-05\n",
      "loss4 :  0.0009524345514364541 loss5 :  2.536773718020413e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.40353143215179443\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.011353587731719017 loss2 :  0.08218560367822647 loss3 :  0.003374338150024414\n",
      "loss4 :  0.04808054119348526 loss5 :  0.2585371732711792\n",
      "Iteration :  7  /  7\n",
      "loss :  0.006492519285529852\n",
      "lossl :  0.0 loss1 :  2.8610230629055877e-07 loss2 :  0.004205894656479359 loss3 :  3.814697265625e-06\n",
      "loss4 :  0.0022211074829101562 loss5 :  6.141662743175402e-05\n",
      "time taken :  0.5736010074615479\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.07954568415880203\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0002891540643759072 loss2 :  0.0002495765802450478 loss3 :  0.0024331093300133944\n",
      "loss4 :  0.006408119108527899 loss5 :  0.07016553729772568\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003895711852237582\n",
      "lossl :  0.0 loss1 :  0.0016045093070715666 loss2 :  0.00026340485783293843 loss3 :  0.0007425307994708419\n",
      "loss4 :  0.0005812644958496094 loss5 :  0.0007040023920126259\n",
      "Iteration :  7  /  7\n",
      "loss :  0.02511310763657093\n",
      "lossl :  0.0 loss1 :  0.013684749603271484 loss2 :  0.00384693150408566 loss3 :  0.005698013119399548\n",
      "loss4 :  0.0017329215770587325 loss5 :  0.0001504898100392893\n",
      "time taken :  0.20600557327270508\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 164/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005814933683723211\n",
      "lossl :  1.9073486612342094e-07 loss1 :  2.2220610844669864e-05 loss2 :  0.0008870124584063888 loss3 :  0.0026960372924804688\n",
      "loss4 :  0.0006077766302041709 loss5 :  0.0016016960144042969\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01081223413348198\n",
      "lossl :  0.0 loss1 :  1.697540210443549e-05 loss2 :  0.006301069166511297 loss3 :  0.0023407936096191406\n",
      "loss4 :  5.3882598876953125e-05 loss5 :  0.0020995140075683594\n",
      "Iteration :  7  /  7\n",
      "loss :  0.047643668949604034\n",
      "lossl :  0.0 loss1 :  0.0025229931343346834 loss2 :  0.0029125213623046875 loss3 :  0.006621933076530695\n",
      "loss4 :  0.035317860543727875 loss5 :  0.00026836394681595266\n",
      "time taken :  0.6411788463592529\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0013102054363116622\n",
      "lossl :  0.0 loss1 :  0.000813341117464006 loss2 :  3.738403393072076e-05 loss3 :  0.0002799987851176411\n",
      "loss4 :  0.00011310577247058973 loss5 :  6.637573096668348e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.002026462694630027\n",
      "lossl :  0.0 loss1 :  0.0001655578671488911 loss2 :  0.0017910003662109375 loss3 :  3.82423386326991e-05\n",
      "loss4 :  1.850128137448337e-05 loss5 :  1.316070574830519e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010089874267578125\n",
      "lossl :  0.0 loss1 :  5.283355858409777e-05 loss2 :  0.0002582549932412803 loss3 :  0.00015554428682662547\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss4 :  0.0001415252627339214 loss5 :  0.0004008293035440147\n",
      "time taken :  0.19539332389831543\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 165/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0722665786743164\n",
      "lossl :  0.0 loss1 :  0.006348609924316406 loss2 :  0.04544887691736221 loss3 :  0.015172290615737438\n",
      "loss4 :  0.004507541656494141 loss5 :  0.0007892608409747481\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003797817276790738\n",
      "lossl :  0.0 loss1 :  3.089904930675402e-05 loss2 :  0.0015194893348962069 loss3 :  0.00013322829909157008\n",
      "loss4 :  0.0018646239768713713 loss5 :  0.0002495765802450478\n",
      "Iteration :  7  /  7\n",
      "loss :  0.002097988035529852\n",
      "lossl :  9.536743306171047e-08 loss1 :  5.950927879894152e-05 loss2 :  0.000904941582120955 loss3 :  8.78334030858241e-05\n",
      "loss4 :  0.0008020401000976562 loss5 :  0.00024356841458939016\n",
      "time taken :  0.6329236030578613\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0004195213259663433\n",
      "lossl :  0.0 loss1 :  0.00010604858107399195 loss2 :  7.2479248046875e-05 loss3 :  0.00015611648268532008\n",
      "loss4 :  5.779266211902723e-05 loss5 :  2.708435022213962e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.06648044288158417\n",
      "lossl :  7.629394644936838e-07 loss1 :  0.0003284454287495464 loss2 :  0.00043573378934524953 loss3 :  0.0015197753673419356\n",
      "loss4 :  0.006739997770637274 loss5 :  0.05745572969317436\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0015254974132403731\n",
      "lossl :  0.0 loss1 :  5.836486889165826e-05 loss2 :  0.00045900343684479594 loss3 :  0.00012969970703125\n",
      "loss4 :  0.0003185272216796875 loss5 :  0.000559902167879045\n",
      "time taken :  0.18509840965270996\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 166/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.055184505879879\n",
      "lossl :  1.23977656585339e-06 loss1 :  0.00859818421304226 loss2 :  0.010018778033554554 loss3 :  0.011450720019638538\n",
      "loss4 :  0.0029544830322265625 loss5 :  0.022161101922392845\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0020270347595214844\n",
      "lossl :  0.0 loss1 :  0.00020227432833053172 loss2 :  0.0005488395690917969 loss3 :  0.00018720627122092992\n",
      "loss4 :  0.000804042792879045 loss5 :  0.0002846717834472656\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0017995834350585938\n",
      "lossl :  1.430511474609375e-06 loss1 :  0.00023527145094703883 loss2 :  0.0004540443478617817 loss3 :  0.00041484832763671875\n",
      "loss4 :  0.0005855560302734375 loss5 :  0.00010843276686500758\n",
      "time taken :  0.5788025856018066\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0008605003240518272\n",
      "lossl :  0.0 loss1 :  0.00041704176692292094 loss2 :  6.599425978492945e-05 loss3 :  0.00023889541625976562\n",
      "loss4 :  9.984969801735133e-05 loss5 :  3.871917579090223e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.019156550988554955\n",
      "lossl :  0.0 loss1 :  0.015735913068056107 loss2 :  4.024505687993951e-05 loss3 :  0.002596950624138117\n",
      "loss4 :  0.0006901741144247353 loss5 :  9.32693510549143e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.3801526129245758\n",
      "lossl :  5.722046125811175e-07 loss1 :  0.0008827209239825606 loss2 :  0.03844099119305611 loss3 :  0.29507961869239807\n",
      "loss4 :  0.045233823359012604 loss5 :  0.0005148887867107987\n",
      "time taken :  0.19353079795837402\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 167/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.01065292302519083\n",
      "lossl :  0.0 loss1 :  9.5367431640625e-07 loss2 :  0.009802818298339844 loss3 :  0.00045566557673737407\n",
      "loss4 :  0.0003228187561035156 loss5 :  7.06672653905116e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0031654357444494963\n",
      "lossl :  0.0 loss1 :  0.0002882957342080772 loss2 :  0.0016256332164630294 loss3 :  0.00015354156494140625\n",
      "loss4 :  0.0006023406749591231 loss5 :  0.0004956245538778603\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005891990847885609\n",
      "lossl :  0.0 loss1 :  0.00014276504225563258 loss2 :  0.004160022828727961 loss3 :  0.0005764961242675781\n",
      "loss4 :  0.0003495216369628906 loss5 :  0.0006631851429119706\n",
      "time taken :  0.5881335735321045\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0007711410871706903\n",
      "lossl :  0.0 loss1 :  7.43865984986769e-06 loss2 :  0.0002484321594238281 loss3 :  3.967285010730848e-05\n",
      "loss4 :  0.00023555755615234375 loss5 :  0.00024003982252907008\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0004084586980752647\n",
      "lossl :  0.0 loss1 :  0.0001834869326557964 loss2 :  1.945495569088962e-05 loss3 :  8.859634544933215e-05\n",
      "loss4 :  9.54627976170741e-05 loss5 :  2.1457672119140625e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009925746358931065\n",
      "lossl :  0.0 loss1 :  0.005659771151840687 loss2 :  5.397796485340223e-05 loss3 :  0.0032317161094397306\n",
      "loss4 :  0.00043783188448287547 loss5 :  0.0005424499395303428\n",
      "time taken :  0.187361478805542\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 168/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0015328406589105725\n",
      "lossl :  2.002715973503655e-06 loss1 :  0.00011377334885764867 loss2 :  0.0009499549632892013 loss3 :  5.397796485340223e-05\n",
      "loss4 :  0.00023717879957985133 loss5 :  0.00017595291137695312\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0016311645740643144\n",
      "lossl :  0.0 loss1 :  3.108978125965223e-05 loss2 :  0.0006650924915447831 loss3 :  0.0001731872616801411\n",
      "loss4 :  0.0007383346674032509 loss5 :  2.346038854739163e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.024778176099061966\n",
      "lossl :  0.0 loss1 :  1.9073486612342094e-07 loss2 :  0.0031210898887366056 loss3 :  0.017559146508574486\n",
      "loss4 :  0.000255584716796875 loss5 :  0.0038421631325036287\n",
      "time taken :  0.5820703506469727\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0010200501419603825\n",
      "lossl :  0.0 loss1 :  0.00023756027803756297 loss2 :  3.643035961431451e-05 loss3 :  0.00018005371384788305\n",
      "loss4 :  0.0004807472287211567 loss5 :  8.525848534191027e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0028491972479969263\n",
      "lossl :  0.0 loss1 :  0.0001848220854299143 loss2 :  0.00014381408982444555 loss3 :  0.0012222289806231856\n",
      "loss4 :  0.0005999564891681075 loss5 :  0.0006983756902627647\n",
      "Iteration :  7  /  7\n",
      "loss :  0.05169658735394478\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0001350402890238911 loss2 :  0.00023202896409202367 loss3 :  0.00040302277193404734\n",
      "loss4 :  0.006434821989387274 loss5 :  0.0444914810359478\n",
      "time taken :  0.18681883811950684\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 169/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0022704126313328743\n",
      "lossl :  0.0 loss1 :  0.00026826857356354594 loss2 :  0.0014165878528729081 loss3 :  0.0002952575741801411\n",
      "loss4 :  0.00010452270362293348 loss5 :  0.00018577575974632055\n",
      "Iteration :  4  /  7\n",
      "loss :  0.17797589302062988\n",
      "lossl :  5.722046125811175e-07 loss1 :  2.899169885495212e-05 loss2 :  0.0004467964172363281 loss3 :  0.14978985488414764\n",
      "loss4 :  0.006405639462172985 loss5 :  0.021304035559296608\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0020383833907544613\n",
      "lossl :  1.62124638336536e-06 loss1 :  0.0007261276477947831 loss2 :  0.0005155563121661544 loss3 :  0.00011949539475608617\n",
      "loss4 :  0.0004409789980854839 loss5 :  0.0002346038818359375\n",
      "time taken :  0.6565248966217041\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0017760276095941663\n",
      "lossl :  0.0 loss1 :  0.00020923613919876516 loss2 :  0.00020570754713844508 loss3 :  0.0008622169261798263\n",
      "loss4 :  0.0002690315304789692 loss5 :  0.00022983551025390625\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0033337592612951994\n",
      "lossl :  0.0 loss1 :  4.730224463855848e-05 loss2 :  0.0010370254749432206 loss3 :  0.0002593040408100933\n",
      "loss4 :  0.0010137557983398438 loss5 :  0.0009763717534951866\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0019431114196777344\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lossl :  5.722046125811175e-07 loss1 :  4.38690185546875e-05 loss2 :  0.0005826950073242188 loss3 :  0.00023574828810524195\n",
      "loss4 :  0.0005182266468182206 loss5 :  0.0005620002630166709\n",
      "time taken :  0.18566060066223145\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 170/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.2720286250114441\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.0002841949462890625 loss2 :  0.17944017052650452 loss3 :  0.01286778412759304\n",
      "loss4 :  0.04045114666223526 loss5 :  0.03898496553301811\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0053338524885475636\n",
      "lossl :  1.5258789289873675e-06 loss1 :  0.0004561901150736958 loss2 :  0.002242565155029297 loss3 :  0.0011714935535565019\n",
      "loss4 :  0.0008714675786904991 loss5 :  0.0005906104925088584\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0001451492280466482\n",
      "lossl :  0.0 loss1 :  0.0 loss2 :  4.76837158203125e-05 loss3 :  2.021789623540826e-05\n",
      "loss4 :  3.089904930675402e-05 loss5 :  4.634857032215223e-05\n",
      "time taken :  0.6357440948486328\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0016179083613678813\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.00042810439481399953 loss2 :  0.00020112990750931203 loss3 :  0.0007379531743936241\n",
      "loss4 :  0.0001623153657419607 loss5 :  8.811950829112902e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0025396347045898438\n",
      "lossl :  1.9073486612342094e-07 loss1 :  2.8705597287626006e-05 loss2 :  0.0003986358642578125 loss3 :  0.0012449264759197831\n",
      "loss4 :  0.00040493012056685984 loss5 :  0.00046224595280364156\n",
      "Iteration :  7  /  7\n",
      "loss :  0.08717118203639984\n",
      "lossl :  0.0 loss1 :  0.00016088485426735133 loss2 :  0.00926980935037136 loss3 :  0.05468731001019478\n",
      "loss4 :  0.02297954633831978 loss5 :  7.362365431617945e-05\n",
      "time taken :  0.18078374862670898\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 171/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.000240325927734375\n",
      "lossl :  0.0 loss1 :  1.430511474609375e-06 loss2 :  1.602172778802924e-05 loss3 :  1.487731969973538e-05\n",
      "loss4 :  0.00020284652418922633 loss5 :  5.14984139954322e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0005620956653729081\n",
      "lossl :  0.0 loss1 :  8.678436643094756e-06 loss2 :  0.00028734205989167094 loss3 :  2.899169885495212e-05\n",
      "loss4 :  7.07626313669607e-05 loss5 :  0.0001663207949604839\n",
      "Iteration :  7  /  7\n",
      "loss :  0.008554553613066673\n",
      "lossl :  0.0 loss1 :  0.0068359375 loss2 :  6.389617738022935e-06 loss3 :  0.0008290290716104209\n",
      "loss4 :  0.0008368492126464844 loss5 :  4.634857032215223e-05\n",
      "time taken :  0.6148462295532227\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.04119863733649254\n",
      "lossl :  0.0 loss1 :  0.0007645607111044228 loss2 :  0.0019360542064532638 loss3 :  0.020449543371796608\n",
      "loss4 :  0.017955303192138672 loss5 :  9.317397780250758e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0006577491876669228\n",
      "lossl :  0.0 loss1 :  0.00026617050752975047 loss2 :  5.91278076171875e-05 loss3 :  0.00016946792311500758\n",
      "loss4 :  0.00012369155592750758 loss5 :  3.929138256353326e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0007996559143066406\n",
      "lossl :  0.0 loss1 :  2.574920654296875e-05 loss2 :  0.00020551681518554688 loss3 :  0.00022258757962845266\n",
      "loss4 :  0.00021467209444381297 loss5 :  0.00013113021850585938\n",
      "time taken :  0.19086360931396484\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 172/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.31055137515068054\n",
      "lossl :  0.0 loss1 :  0.2669203281402588 loss2 :  3.299712989246473e-05 loss3 :  0.03959999233484268\n",
      "loss4 :  0.0008622169261798263 loss5 :  0.0031358718406409025\n",
      "Iteration :  4  /  7\n",
      "loss :  0.07326555252075195\n",
      "lossl :  0.0 loss1 :  0.0076118470169603825 loss2 :  0.0019098281627520919 loss3 :  0.00911931972950697\n",
      "loss4 :  0.004510259721428156 loss5 :  0.05011429637670517\n",
      "Iteration :  7  /  7\n",
      "loss :  0.1360522210597992\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.059000302106142044 loss2 :  0.0035703659523278475 loss3 :  0.010100269690155983\n",
      "loss4 :  0.038729093968868256 loss5 :  0.02465200424194336\n",
      "time taken :  0.6098761558532715\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0006528854719363153\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.00021181107149459422 loss2 :  0.00011110305786132812 loss3 :  0.0001678466796875\n",
      "loss4 :  0.00010204315185546875 loss5 :  5.9795380366267636e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0009737968211993575\n",
      "lossl :  0.0 loss1 :  1.2302398317842744e-05 loss2 :  3.528594970703125e-05 loss3 :  0.0008123397710733116\n",
      "loss4 :  2.8705597287626006e-05 loss5 :  8.516311936546117e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010547637939453125\n",
      "lossl :  0.0 loss1 :  8.96453857421875e-05 loss2 :  0.0007875442388467491 loss3 :  0.00010967254638671875\n",
      "loss4 :  4.00543212890625e-05 loss5 :  2.784729076665826e-05\n",
      "time taken :  0.1959977149963379\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 173/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0003604888916015625\n",
      "lossl :  0.0 loss1 :  4.1961669921875e-05 loss2 :  3.0517578125e-05 loss3 :  7.343292236328125e-05\n",
      "loss4 :  9.880065772449598e-05 loss5 :  0.00011577606346691027\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0009182929643429816\n",
      "lossl :  0.0 loss1 :  1.811981201171875e-05 loss2 :  0.0005366325494833291 loss3 :  0.0001319885195698589\n",
      "loss4 :  2.670288040462765e-06 loss5 :  0.0002288818359375\n",
      "Iteration :  7  /  7\n",
      "loss :  0.007789229974150658\n",
      "lossl :  0.0 loss1 :  6.179809861350805e-05 loss2 :  0.0006072044488973916 loss3 :  0.0035017014015465975\n",
      "loss4 :  0.0026526451110839844 loss5 :  0.0009658813360147178\n",
      "time taken :  0.5937581062316895\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0010776519775390625\n",
      "lossl :  0.0 loss1 :  0.0009853362571448088 loss2 :  7.915497008070815e-06 loss3 :  3.585815284168348e-05\n",
      "loss4 :  1.468658410885837e-05 loss5 :  3.3855438232421875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.009661484509706497\n",
      "lossl :  0.0 loss1 :  0.00011577606346691027 loss2 :  0.0004984855768270791 loss3 :  0.005615329835563898\n",
      "loss4 :  0.0033389092423021793 loss5 :  9.298324584960938e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0010577202774584293\n",
      "lossl :  0.0 loss1 :  8.049011375987902e-05 loss2 :  0.000796413398347795 loss3 :  0.00011138916306663305\n",
      "loss4 :  4.806518700206652e-05 loss5 :  2.136230432370212e-05\n",
      "time taken :  0.18892478942871094\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 174/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.005328178405761719\n",
      "lossl :  0.0 loss1 :  0.0029316903091967106 loss2 :  0.00046482085599564016 loss3 :  0.00017242431931663305\n",
      "loss4 :  0.00021982192993164062 loss5 :  0.00153942103497684\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0059805866330862045\n",
      "lossl :  0.0 loss1 :  0.0003056526184082031 loss2 :  0.0003879547002725303 loss3 :  0.0036181448958814144\n",
      "loss4 :  0.0004542350652627647 loss5 :  0.0012145995860919356\n",
      "Iteration :  7  /  7\n",
      "loss :  0.11233317852020264\n",
      "lossl :  0.0 loss1 :  0.000255584716796875 loss2 :  0.06780479103326797 loss3 :  0.003524207975715399\n",
      "loss4 :  0.032370567321777344 loss5 :  0.008378028869628906\n",
      "time taken :  0.5808162689208984\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002316379686817527\n",
      "lossl :  0.0 loss1 :  5.836486889165826e-05 loss2 :  9.93728608591482e-05 loss3 :  0.000994014786556363\n",
      "loss4 :  0.0011568069458007812 loss5 :  7.82012921263231e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0012765884166583419\n",
      "lossl :  0.0 loss1 :  0.0005169868236407638 loss2 :  1.52587890625e-05 loss3 :  0.0006651878356933594\n",
      "loss4 :  2.365112231927924e-05 loss5 :  5.550384594243951e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00139703752938658\n",
      "lossl :  0.0 loss1 :  6.408691115211695e-05 loss2 :  7.209777686512098e-05 loss3 :  4.482269287109375e-05\n",
      "loss4 :  0.0006567955133505166 loss5 :  0.0005592346424236894\n",
      "time taken :  0.18521475791931152\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 175/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.011678790673613548\n",
      "lossl :  0.0 loss1 :  0.00011539459228515625 loss2 :  0.0003165244997944683 loss3 :  0.008591746911406517\n",
      "loss4 :  0.002399540040642023 loss5 :  0.000255584716796875\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0016128539573401213\n",
      "lossl :  0.0 loss1 :  9.72747784544481e-06 loss2 :  0.00030221938504837453 loss3 :  9.15527380129788e-06\n",
      "loss4 :  0.0012867928016930819 loss5 :  4.95910626341356e-06\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0034071921836584806\n",
      "lossl :  0.0 loss1 :  0.0025273323990404606 loss2 :  0.0003127098025288433 loss3 :  0.0003982544003520161\n",
      "loss4 :  0.0001029014601954259 loss5 :  6.599425978492945e-05\n",
      "time taken :  0.5780279636383057\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00033321380033157766\n",
      "lossl :  0.0 loss1 :  7.123946852516383e-05 loss2 :  7.333755638683215e-05 loss3 :  9.078979201149195e-05\n",
      "loss4 :  2.021789623540826e-05 loss5 :  7.762909081066027e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00038013458834029734\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.000240325927734375 loss2 :  1.6498564946232364e-05 loss3 :  9.193420555675402e-05\n",
      "loss4 :  1.52587890625e-05 loss5 :  1.5926361811580136e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00091047293972224\n",
      "lossl :  0.0 loss1 :  0.00021295547776389867 loss2 :  2.727508581301663e-05 loss3 :  0.00022745132446289062\n",
      "loss4 :  0.00042428969754837453 loss5 :  1.850128137448337e-05\n",
      "time taken :  0.18149423599243164\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 176/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.006719112396240234\n",
      "lossl :  0.0 loss1 :  0.00018444060697220266 loss2 :  0.000620937324129045 loss3 :  0.0001485824614064768\n",
      "loss4 :  0.00494728097692132 loss5 :  0.0008178710704669356\n",
      "Iteration :  4  /  7\n",
      "loss :  0.19175314903259277\n",
      "lossl :  1.62124638336536e-06 loss1 :  0.18711166083812714 loss2 :  0.0006105423090048134 loss3 :  0.0028311251662671566\n",
      "loss4 :  0.0006872176891192794 loss5 :  0.0005109786870889366\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0013929366832599044\n",
      "lossl :  0.0 loss1 :  7.944107346702367e-05 loss2 :  9.498596045887098e-05 loss3 :  0.0004952430608682334\n",
      "loss4 :  0.00030412673368118703 loss5 :  0.0004191398620605469\n",
      "time taken :  0.5928847789764404\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0023920058738440275\n",
      "lossl :  0.0 loss1 :  6.942749314475805e-05 loss2 :  4.2438507080078125e-05 loss3 :  0.0005172729725018144\n",
      "loss4 :  0.0017502785194665194 loss5 :  1.258850079466356e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0007363319164142013\n",
      "lossl :  0.0 loss1 :  1.8596649169921875e-05 loss2 :  6.179809861350805e-05 loss3 :  0.0005586624029092491\n",
      "loss4 :  3.223419116693549e-05 loss5 :  6.50405854685232e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0014160156715661287\n",
      "lossl :  0.0 loss1 :  0.0013464927906170487 loss2 :  2.2411346435546875e-05 loss3 :  1.106262243411038e-05\n",
      "loss4 :  1.945495569088962e-05 loss5 :  1.659393274167087e-05\n",
      "time taken :  0.1877446174621582\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 177/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.29721301794052124\n",
      "lossl :  0.0 loss1 :  0.005924844648689032 loss2 :  0.23880191147327423 loss3 :  0.008940314874053001\n",
      "loss4 :  0.0018642426002770662 loss5 :  0.041681669652462006\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0009708404541015625\n",
      "lossl :  1.9073486612342094e-07 loss1 :  3.24249276673072e-06 loss2 :  2.6226043701171875e-05 loss3 :  1.640319896978326e-05\n",
      "loss4 :  0.0005273818969726562 loss5 :  0.00039739609928801656\n",
      "Iteration :  7  /  7\n",
      "loss :  0.009508037008345127\n",
      "lossl :  2.28881845032447e-06 loss1 :  0.007149410434067249 loss2 :  0.00034847258939407766 loss3 :  0.0008922576671466231\n",
      "loss4 :  0.0004677772521972656 loss5 :  0.0006478309514932334\n",
      "time taken :  0.5879421234130859\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00018434524827171117\n",
      "lossl :  0.0 loss1 :  6.723403930664062e-05 loss2 :  2.079009937006049e-05 loss3 :  3.833770824712701e-05\n",
      "loss4 :  5.016326758777723e-05 loss5 :  7.82012921263231e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0009981155162677169\n",
      "lossl :  0.0 loss1 :  0.00019130707369185984 loss2 :  0.00010271072096657008 loss3 :  0.0005696296575479209\n",
      "loss4 :  5.14984139954322e-06 loss5 :  0.0001293182431254536\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0002658843877725303\n",
      "lossl :  0.0 loss1 :  0.00012750625319313258 loss2 :  8.56399565236643e-05 loss3 :  2.5653838747530244e-05\n",
      "loss4 :  1.201629675051663e-05 loss5 :  1.506805438111769e-05\n",
      "time taken :  0.18732404708862305\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 178/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008217143826186657\n",
      "lossl :  9.536743306171047e-08 loss1 :  0.0006915092235431075 loss2 :  0.0003661155642475933 loss3 :  0.0011227608192712069\n",
      "loss4 :  0.0003135681035928428 loss5 :  0.005723095033317804\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0011141777504235506\n",
      "lossl :  0.0 loss1 :  0.0002533912775106728 loss2 :  1.811981201171875e-05 loss3 :  0.000202178955078125\n",
      "loss4 :  0.0006135940784588456 loss5 :  2.689361645025201e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0006296157953329384\n",
      "lossl :  0.0 loss1 :  1.220703143189894e-05 loss2 :  0.0001600265532033518 loss3 :  9.250640869140625e-05\n",
      "loss4 :  3.566742088878527e-05 loss5 :  0.0003292083856649697\n",
      "time taken :  0.5792207717895508\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.004941749386489391\n",
      "lossl :  0.0 loss1 :  0.00027341843815520406 loss2 :  0.0002000808744924143 loss3 :  0.0008523940923623741\n",
      "loss4 :  0.0008519172552041709 loss5 :  0.002763938857242465\n",
      "Iteration :  4  /  7\n",
      "loss :  0.000560760498046875\n",
      "lossl :  0.0 loss1 :  0.0004591941833496094 loss2 :  4.367828296381049e-05 loss3 :  1.029968279908644e-05\n",
      "loss4 :  5.91278057981981e-06 loss5 :  4.1675568354548886e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00036582944449037313\n",
      "lossl :  0.0 loss1 :  0.00013532637967728078 loss2 :  2.403259350103326e-05 loss3 :  0.00016546249389648438\n",
      "loss4 :  2.555847095209174e-05 loss5 :  1.544952465337701e-05\n",
      "time taken :  0.1867215633392334\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 179/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0022722245194017887\n",
      "lossl :  0.0 loss1 :  0.00010643005225574598 loss2 :  0.00011301040649414062 loss3 :  6.837844557594508e-05\n",
      "loss4 :  0.0005248069646768272 loss5 :  0.0014595985412597656\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01578846015036106\n",
      "lossl :  0.0 loss1 :  3.43322744811303e-06 loss2 :  0.005035877227783203 loss3 :  0.008273315615952015\n",
      "loss4 :  0.0023555755615234375 loss5 :  0.0001202583298436366\n",
      "Iteration :  7  /  7\n",
      "loss :  0.015910077840089798\n",
      "lossl :  0.0 loss1 :  3.814697265625e-06 loss2 :  0.007764649577438831 loss3 :  0.007636833004653454\n",
      "loss4 :  0.0001815795840229839 loss5 :  0.00032320022000931203\n",
      "time taken :  0.5853331089019775\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00044651032658293843\n",
      "lossl :  0.0 loss1 :  5.7697296142578125e-05 loss2 :  8.94546537892893e-05 loss3 :  0.0001562118559377268\n",
      "loss4 :  0.00011854172043967992 loss5 :  2.460479663568549e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0028683661948889494\n",
      "lossl :  0.0 loss1 :  0.0016565322875976562 loss2 :  0.0002518653927836567 loss3 :  0.0006492614629678428\n",
      "loss4 :  0.0001087188720703125 loss5 :  0.0002019882231252268\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004001617431640625\n",
      "lossl :  0.0 loss1 :  0.00031023024348542094 loss2 :  3.871917579090223e-05 loss3 :  0.0004040718195028603\n",
      "loss4 :  0.0032415390014648438 loss5 :  7.05719003235572e-06\n",
      "time taken :  0.18590903282165527\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 180/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.003174209501594305\n",
      "lossl :  0.0 loss1 :  0.00026979445829056203 loss2 :  0.0021888732444494963 loss3 :  0.00018033981905318797\n",
      "loss4 :  0.0004899025079794228 loss5 :  4.5299530029296875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.10240554809570312\n",
      "lossl :  1.716613724056515e-06 loss1 :  5.722046125811175e-07 loss2 :  0.0004591941833496094 loss3 :  0.0701751708984375\n",
      "loss4 :  0.00272026052698493 loss5 :  0.029048632830381393\n",
      "Iteration :  7  /  7\n",
      "loss :  0.022475432604551315\n",
      "lossl :  0.0 loss1 :  0.00012102127220714465 loss2 :  0.00029048920259810984 loss3 :  0.007116222288459539\n",
      "loss4 :  0.00019779204740189016 loss5 :  0.014749908819794655\n",
      "time taken :  0.588284969329834\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0007179260137490928\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0004122733953408897 loss2 :  5.111694190418348e-05 loss3 :  0.0002080917329294607\n",
      "loss4 :  3.147125244140625e-05 loss5 :  1.468658410885837e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0019258499378338456\n",
      "lossl :  0.0 loss1 :  0.001413059188053012 loss2 :  0.00015077591524459422 loss3 :  6.418228440452367e-05\n",
      "loss4 :  0.00012540817260742188 loss5 :  0.00017242431931663305\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00019979475473519415\n",
      "lossl :  0.0 loss1 :  9.260177466785535e-05 loss2 :  7.171630568336695e-05 loss3 :  1.220703143189894e-05\n",
      "loss4 :  1.373290979245212e-05 loss5 :  9.5367431640625e-06\n",
      "time taken :  0.188551664352417\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 181/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.020902013406157494\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.005098104476928711 loss2 :  0.0007802963373251259 loss3 :  0.0004920006031170487\n",
      "loss4 :  0.0012094497215002775 loss5 :  0.013321876525878906\n",
      "Iteration :  4  /  7\n",
      "loss :  0.019835758954286575\n",
      "lossl :  0.0 loss1 :  0.003416347550228238 loss2 :  0.0010204315185546875 loss3 :  0.015353393740952015\n",
      "loss4 :  6.10351571594947e-06 loss5 :  3.948211815441027e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.001277065253816545\n",
      "lossl :  0.0 loss1 :  2.098083541568485e-06 loss2 :  0.0008311271667480469 loss3 :  0.00035839079646393657\n",
      "loss4 :  9.15527380129788e-06 loss5 :  7.62939453125e-05\n",
      "time taken :  0.575822114944458\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00031299592228606343\n",
      "lossl :  0.0 loss1 :  1.8596649169921875e-05 loss2 :  5.91278076171875e-05 loss3 :  1.602172778802924e-05\n",
      "loss4 :  0.00015020370483398438 loss5 :  6.904602196300402e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0002198219153797254\n",
      "lossl :  0.0 loss1 :  1.7642974853515625e-05 loss2 :  9.72747802734375e-05 loss3 :  4.711151268566027e-05\n",
      "loss4 :  4.310607982915826e-05 loss5 :  1.468658410885837e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.011800384148955345\n",
      "lossl :  0.0 loss1 :  0.0003501892206259072 loss2 :  0.0005030632019042969 loss3 :  0.0017543792491778731\n",
      "loss4 :  0.009102439507842064 loss5 :  9.031295485328883e-05\n",
      "time taken :  0.19887208938598633\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 182/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0033294674940407276\n",
      "lossl :  1.23977656585339e-06 loss1 :  0.0013081550132483244 loss2 :  9.574890282237902e-05 loss3 :  0.0009274482727050781\n",
      "loss4 :  0.0009097099537029862 loss5 :  8.716583397472277e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.4454692602157593\n",
      "lossl :  0.0 loss1 :  6.513595872092992e-05 loss2 :  0.23315401375293732 loss3 :  0.001706790877506137\n",
      "loss4 :  0.0028706551529467106 loss5 :  0.20767268538475037\n",
      "Iteration :  7  /  7\n",
      "loss :  0.003726100781932473\n",
      "lossl :  1.9073486612342094e-07 loss1 :  2.3555756342830136e-05 loss2 :  0.002744388533756137 loss3 :  0.0001415252627339214\n",
      "loss4 :  0.00038270949153229594 loss5 :  0.0004337310674600303\n",
      "time taken :  0.5822615623474121\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00047893525334075093\n",
      "lossl :  0.0 loss1 :  5.855560448253527e-05 loss2 :  0.00029773713322356343 loss3 :  6.017684791004285e-05\n",
      "loss4 :  9.5367431640625e-06 loss5 :  5.2928924560546875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00010385512723587453\n",
      "lossl :  0.0 loss1 :  1.5258789289873675e-06 loss2 :  3.890991138177924e-05 loss3 :  1.506805438111769e-05\n",
      "loss4 :  2.956390380859375e-05 loss5 :  1.8787384760798886e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0037689208984375\n",
      "lossl :  0.0 loss1 :  0.00013666153245139867 loss2 :  3.137588646495715e-05 loss3 :  9.975433204090223e-05\n",
      "loss4 :  0.0004505157412495464 loss5 :  0.0030506134498864412\n",
      "time taken :  0.18635964393615723\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 183/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0016995430923998356\n",
      "lossl :  0.0 loss1 :  0.0005981445428915322 loss2 :  3.223419116693549e-05 loss3 :  0.00028514862060546875\n",
      "loss4 :  0.00014963149442337453 loss5 :  0.0006343841669149697\n",
      "Iteration :  4  /  7\n",
      "loss :  0.11549106240272522\n",
      "lossl :  0.0 loss1 :  5.092620995128527e-05 loss2 :  0.0023404122330248356 loss3 :  0.11183857917785645\n",
      "loss4 :  0.00042552949162200093 loss5 :  0.0008356094476766884\n",
      "Iteration :  7  /  7\n",
      "loss :  0.17258009314537048\n",
      "lossl :  5.722046125811175e-07 loss1 :  2.7561187380342744e-05 loss2 :  0.0024248124100267887 loss3 :  0.00022344589524436742\n",
      "loss4 :  0.0012552260886877775 loss5 :  0.16864848136901855\n",
      "time taken :  0.5714550018310547\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002499199006706476\n",
      "lossl :  0.0 loss1 :  0.00013380050950217992 loss2 :  1.9168854123563506e-05 loss3 :  1.926422191900201e-05\n",
      "loss4 :  0.0004566192510537803 loss5 :  0.0018703460227698088\n",
      "Iteration :  4  /  7\n",
      "loss :  0.000118255615234375\n",
      "lossl :  0.0 loss1 :  1.3351440202313825e-06 loss2 :  3.62396240234375e-05 loss3 :  2.365112231927924e-05\n",
      "loss4 :  3.62396240234375e-05 loss5 :  2.079009937006049e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00301017751917243\n",
      "lossl :  0.0 loss1 :  4.215240551275201e-05 loss2 :  0.0028099059127271175 loss3 :  0.00012102127220714465\n",
      "loss4 :  2.8705597287626006e-05 loss5 :  8.39233416627394e-06\n",
      "time taken :  0.19977355003356934\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 184/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05010261386632919\n",
      "lossl :  0.0 loss1 :  0.005396080203354359 loss2 :  0.004725551698356867 loss3 :  0.0034208297729492188\n",
      "loss4 :  0.00019569396681617945 loss5 :  0.03636445850133896\n",
      "Iteration :  4  /  7\n",
      "loss :  0.029096413403749466\n",
      "lossl :  0.0 loss1 :  3.3855438232421875e-05 loss2 :  0.01621065102517605 loss3 :  0.0026763915084302425\n",
      "loss4 :  0.0012519836891442537 loss5 :  0.008923530578613281\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00429878244176507\n",
      "lossl :  0.0 loss1 :  6.961822691664565e-06 loss2 :  0.00030307768611237407 loss3 :  0.003253078553825617\n",
      "loss4 :  5.779266211902723e-05 loss5 :  0.0006778716924600303\n",
      "time taken :  0.575507402420044\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  8.344650996150449e-05\n",
      "lossl :  0.0 loss1 :  1.068115216185106e-05 loss2 :  8.58306884765625e-06 loss3 :  4.18663039454259e-05\n",
      "loss4 :  1.010894811770413e-05 loss5 :  1.220703143189894e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0024690628051757812\n",
      "lossl :  0.0 loss1 :  0.0005021095275878906 loss2 :  0.00010957718041026965 loss3 :  0.00027971266536042094\n",
      "loss4 :  0.0003603935183491558 loss5 :  0.0012172699207440019\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00024299621873069555\n",
      "lossl :  0.0 loss1 :  8.230209641624242e-05 loss2 :  3.337860107421875e-05 loss3 :  1.354217511106981e-05\n",
      "loss4 :  3.5572051274357364e-05 loss5 :  7.82012939453125e-05\n",
      "time taken :  0.18806982040405273\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 185/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.14114657044410706\n",
      "lossl :  0.0 loss1 :  0.0006058692815713584 loss2 :  7.45773286325857e-05 loss3 :  0.1396346092224121\n",
      "loss4 :  3.738403393072076e-05 loss5 :  0.0007941246149130166\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0011959075927734375\n",
      "lossl :  1.9073486328125e-06 loss1 :  8.96453821042087e-06 loss2 :  0.0005605697515420616 loss3 :  0.0003913879336323589\n",
      "loss4 :  1.9073486328125e-05 loss5 :  0.0002140045107807964\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0032760619651526213\n",
      "lossl :  2.8610230629055877e-07 loss1 :  0.0005142212030477822 loss2 :  0.0022533417213708162 loss3 :  4.8923491704044864e-05\n",
      "loss4 :  8.287429955089465e-05 loss5 :  0.00037641526432707906\n",
      "time taken :  0.5810437202453613\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.002494335174560547\n",
      "lossl :  0.0 loss1 :  0.0003186225949320942 loss2 :  0.00010709762864280492 loss3 :  3.032684253412299e-05\n",
      "loss4 :  0.0004654884396586567 loss5 :  0.0015727996360510588\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0011713028652593493\n",
      "lossl :  0.0 loss1 :  5.235672142589465e-05 loss2 :  0.0001108169526560232 loss3 :  0.00018558502779342234\n",
      "loss4 :  0.0007852554554119706 loss5 :  3.728866431629285e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00043058398296125233\n",
      "lossl :  0.0 loss1 :  5.1975250244140625e-05 loss2 :  0.00021419525728560984 loss3 :  1.640319896978326e-05\n",
      "loss4 :  5.168914867681451e-05 loss5 :  9.632110595703125e-05\n",
      "time taken :  0.19788599014282227\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 186/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.014812135137617588\n",
      "lossl :  9.5367431640625e-07 loss1 :  0.0018694878090173006 loss2 :  0.00967097282409668 loss3 :  0.0025815963745117188\n",
      "loss4 :  0.0003993987920694053 loss5 :  0.00028972624568268657\n",
      "Iteration :  4  /  7\n",
      "loss :  0.007480907253921032\n",
      "lossl :  0.0 loss1 :  0.0007143974071368575 loss2 :  0.0016525269020348787 loss3 :  0.004953288938850164\n",
      "loss4 :  0.00013246535672806203 loss5 :  2.822876012942288e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.012938117608428001\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00012645722017623484 loss2 :  0.0030434608925133944 loss3 :  0.0007611274486407638\n",
      "loss4 :  0.0014328956604003906 loss5 :  0.007573985960334539\n",
      "time taken :  0.5739119052886963\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0003699302615132183\n",
      "lossl :  0.0 loss1 :  0.00017280578322242945 loss2 :  2.517700158932712e-05 loss3 :  7.314681715797633e-05\n",
      "loss4 :  6.923675391590223e-05 loss5 :  2.956390380859375e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00011749268014682457\n",
      "lossl :  0.0 loss1 :  3.509521411615424e-05 loss2 :  2.536773718020413e-05 loss3 :  3.070831371587701e-05\n",
      "loss4 :  2.098083541568485e-06 loss5 :  2.422332727292087e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0015485762851312757\n",
      "lossl :  0.0 loss1 :  1.6498564946232364e-05 loss2 :  3.833770824712701e-05 loss3 :  0.0013415336143225431\n",
      "loss4 :  8.430481102550402e-05 loss5 :  6.790160841774195e-05\n",
      "time taken :  0.18886423110961914\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 187/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00023717881413176656\n",
      "lossl :  0.0 loss1 :  1.8119811784345075e-06 loss2 :  2.250671423098538e-05 loss3 :  0.00012264252291060984\n",
      "loss4 :  5.016326758777723e-05 loss5 :  4.00543212890625e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00810461025685072\n",
      "lossl :  3.814697322468419e-07 loss1 :  3.299712989246473e-05 loss2 :  0.008030605502426624 loss3 :  7.24792471373803e-06\n",
      "loss4 :  2.708435022213962e-05 loss5 :  6.29425039733178e-06\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01983063481748104\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.015037131495773792 loss2 :  0.0004405975341796875 loss3 :  0.0035513401962816715\n",
      "loss4 :  0.0007412910345010459 loss5 :  5.989074634271674e-05\n",
      "time taken :  0.5781114101409912\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00013675689115189016\n",
      "lossl :  0.0 loss1 :  4.95910626341356e-06 loss2 :  3.299712989246473e-05 loss3 :  3.776550147449598e-05\n",
      "loss4 :  8.39233416627394e-06 loss5 :  5.264282299322076e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0003192901785951108\n",
      "lossl :  0.0 loss1 :  0.00013360977754928172 loss2 :  7.152557373046875e-05 loss3 :  4.634857032215223e-05\n",
      "loss4 :  5.3119660151423886e-05 loss5 :  1.468658410885837e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004977035336196423\n",
      "lossl :  0.0 loss1 :  0.00010957718041026965 loss2 :  7.62939453125e-05 loss3 :  0.001474571181461215\n",
      "loss4 :  0.0010864257346838713 loss5 :  0.0022301673889160156\n",
      "time taken :  0.1901395320892334\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 188/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0013576507335528731\n",
      "lossl :  0.0 loss1 :  0.000659942626953125 loss2 :  0.00017833709716796875 loss3 :  6.88552827341482e-05\n",
      "loss4 :  0.00023107528977561742 loss5 :  0.00021944046602584422\n",
      "Iteration :  4  /  7\n",
      "loss :  0.20531615614891052\n",
      "lossl :  0.0 loss1 :  0.0005269050598144531 loss2 :  0.20008668303489685 loss3 :  0.000274658203125\n",
      "loss4 :  0.004112052731215954 loss5 :  0.0003158569452352822\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00011014938354492188\n",
      "lossl :  0.0 loss1 :  3.585815284168348e-05 loss2 :  3.890991138177924e-05 loss3 :  1.888275073724799e-05\n",
      "loss4 :  1.2874603271484375e-05 loss5 :  3.623962356869015e-06\n",
      "time taken :  0.5756044387817383\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.001380539033561945\n",
      "lossl :  0.0 loss1 :  3.890991138177924e-05 loss2 :  0.00021467209444381297 loss3 :  0.0004000663757324219\n",
      "loss4 :  0.0007223129505291581 loss5 :  4.57763690064894e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004459190182387829\n",
      "lossl :  0.0 loss1 :  6.828307959949598e-05 loss2 :  1.296997106692288e-05 loss3 :  1.52587890625e-05\n",
      "loss4 :  0.001142215682193637 loss5 :  0.003220462705940008\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0001031875581247732\n",
      "lossl :  0.0 loss1 :  6.961822691664565e-06 loss2 :  3.728866431629285e-05 loss3 :  1.754760705807712e-05\n",
      "loss4 :  5.91278057981981e-06 loss5 :  3.547668529790826e-05\n",
      "time taken :  0.18728876113891602\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 189/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00756988488137722\n",
      "lossl :  0.0 loss1 :  0.0028035163413733244 loss2 :  0.0015190124977380037 loss3 :  0.0015177726745605469\n",
      "loss4 :  0.0011594772804528475 loss5 :  0.0005701064947061241\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01206131000071764\n",
      "lossl :  3.814697322468419e-07 loss1 :  0.003184414003044367 loss2 :  9.93728608591482e-05 loss3 :  0.00012712478928733617\n",
      "loss4 :  0.0081336023285985 loss5 :  0.0005164146423339844\n",
      "Iteration :  7  /  7\n",
      "loss :  0.04903540760278702\n",
      "lossl :  1.1920928955078125e-05 loss1 :  0.009273243136703968 loss2 :  0.003212451934814453 loss3 :  0.006771373562514782\n",
      "loss4 :  0.02170567587018013 loss5 :  0.008060741238296032\n",
      "time taken :  0.5855095386505127\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0026107309386134148\n",
      "lossl :  1.716613724056515e-06 loss1 :  0.0005990028148517013 loss2 :  0.0006042480235919356 loss3 :  0.0008020401000976562\n",
      "loss4 :  3.166198803228326e-05 loss5 :  0.0005720615154132247\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0012575149303302169\n",
      "lossl :  0.0 loss1 :  0.0001184463471872732 loss2 :  5.254745337879285e-05 loss3 :  0.00029840468778274953\n",
      "loss4 :  0.00047817229642532766 loss5 :  0.00030994415283203125\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0002307891845703125\n",
      "lossl :  0.0 loss1 :  1.411438006471144e-05 loss2 :  3.82423386326991e-05 loss3 :  1.201629675051663e-05\n",
      "loss4 :  0.00010347366333007812 loss5 :  6.29425048828125e-05\n",
      "time taken :  0.18671226501464844\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 190/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.03771724924445152\n",
      "lossl :  4.76837158203125e-07 loss1 :  0.00035800933255814016 loss2 :  0.0002140045107807964 loss3 :  0.006361103150993586\n",
      "loss4 :  0.030370140448212624 loss5 :  0.0004135131894145161\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00021295547776389867\n",
      "lossl :  0.0 loss1 :  6.675720101156912e-07 loss2 :  5.7697296142578125e-05 loss3 :  9.5367431640625e-07\n",
      "loss4 :  2.86102294921875e-06 loss5 :  0.00015077591524459422\n",
      "Iteration :  7  /  7\n",
      "loss :  0.5596115589141846\n",
      "lossl :  0.0 loss1 :  4.57763690064894e-06 loss2 :  7.953643944347277e-05 loss3 :  0.13480940461158752\n",
      "loss4 :  0.0031398297287523746 loss5 :  0.42157822847366333\n",
      "time taken :  0.5819394588470459\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0003266334533691406\n",
      "lossl :  0.0 loss1 :  9.15527380129788e-06 loss2 :  1.106262243411038e-05 loss3 :  5.455017162603326e-05\n",
      "loss4 :  0.0002478599490132183 loss5 :  4.00543194700731e-06\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0007333755493164062\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0002673148992471397 loss2 :  0.00014600754366256297 loss3 :  0.00012540817260742188\n",
      "loss4 :  4.844665454584174e-05 loss5 :  0.00014600754366256297\n",
      "Iteration :  7  /  7\n",
      "loss :  0.005552148912101984\n",
      "lossl :  0.0 loss1 :  4.1294097172794864e-05 loss2 :  0.002846622373908758 loss3 :  0.0008633613470010459\n",
      "loss4 :  4.463195728021674e-05 loss5 :  0.0017562389839440584\n",
      "time taken :  0.18892598152160645\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 191/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.6689942479133606\n",
      "lossl :  0.0 loss1 :  0.0032682418823242188 loss2 :  0.0006687164423055947 loss3 :  0.0001850128173828125\n",
      "loss4 :  0.6648060083389282 loss5 :  6.628036499023438e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.697820246219635\n",
      "lossl :  0.0 loss1 :  7.99179106252268e-05 loss2 :  0.6958553791046143 loss3 :  0.0003027915954589844\n",
      "loss4 :  0.0014411925803869963 loss5 :  0.0001409530668752268\n",
      "Iteration :  7  /  7\n",
      "loss :  0.896230936050415\n",
      "lossl :  2.19345088225964e-06 loss1 :  0.011192607693374157 loss2 :  0.23895259201526642 loss3 :  0.012114810757339\n",
      "loss4 :  0.14843225479125977 loss5 :  0.4855364263057709\n",
      "time taken :  0.5748715400695801\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00045680999755859375\n",
      "lossl :  0.0 loss1 :  0.00014600754366256297 loss2 :  2.689361645025201e-05 loss3 :  0.00011720657494151965\n",
      "loss4 :  2.231597864010837e-05 loss5 :  0.00014438628568314016\n",
      "Iteration :  4  /  7\n",
      "loss :  0.005967617034912109\n",
      "lossl :  0.0 loss1 :  2.0885467165498994e-05 loss2 :  0.0021344185806810856 loss3 :  0.0006453514215536416\n",
      "loss4 :  0.00295085902325809 loss5 :  0.00021610260591842234\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01760854572057724\n",
      "lossl :  0.0 loss1 :  6.084442065912299e-05 loss2 :  0.0036716938484460115 loss3 :  0.0059146881103515625\n",
      "loss4 :  0.006835079286247492 loss5 :  0.0011262416373938322\n",
      "time taken :  0.18954253196716309\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 192/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0124365808442235\n",
      "lossl :  1.9073486612342094e-07 loss1 :  1.277923547604587e-05 loss2 :  0.009988022036850452 loss3 :  0.00038356782170012593\n",
      "loss4 :  0.00041484832763671875 loss5 :  0.0016371726524084806\n",
      "Iteration :  4  /  7\n",
      "loss :  0.009463596157729626\n",
      "lossl :  0.0 loss1 :  1.373290979245212e-05 loss2 :  0.0010902404319494963 loss3 :  0.0018223762745037675\n",
      "loss4 :  0.004466152284294367 loss5 :  0.0020710944663733244\n",
      "Iteration :  7  /  7\n",
      "loss :  0.026391983032226562\n",
      "lossl :  0.0 loss1 :  0.004628372378647327 loss2 :  0.0069179534912109375 loss3 :  0.009302711114287376\n",
      "loss4 :  0.0014896392822265625 loss5 :  0.004053306765854359\n",
      "time taken :  0.5785379409790039\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00015134812565520406\n",
      "lossl :  0.0 loss1 :  8.535385131835938e-05 loss2 :  1.411438006471144e-05 loss3 :  1.029968279908644e-05\n",
      "loss4 :  8.39233416627394e-06 loss5 :  3.318786548334174e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0623132698237896\n",
      "lossl :  0.0 loss1 :  0.0002246856747660786 loss2 :  0.001290988875553012 loss3 :  0.028786659240722656\n",
      "loss4 :  0.03177947923541069 loss5 :  0.00023145675368141383\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0002520561101846397\n",
      "lossl :  0.0 loss1 :  5.5027008784236386e-05 loss2 :  2.841949390131049e-05 loss3 :  3.509521411615424e-05\n",
      "loss4 :  2.956390380859375e-05 loss5 :  0.00010395050048828125\n",
      "time taken :  0.18932652473449707\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 193/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.003394078928977251\n",
      "lossl :  0.0 loss1 :  5.984306335449219e-05 loss2 :  7.24792471373803e-06 loss3 :  0.0012704848777502775\n",
      "loss4 :  0.002037429716438055 loss5 :  1.9073486328125e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.04455776512622833\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0021784782875329256 loss2 :  0.0013414382701739669 loss3 :  0.030237197875976562\n",
      "loss4 :  0.006622314453125 loss5 :  0.004178142640739679\n",
      "Iteration :  7  /  7\n",
      "loss :  0.004709816072136164\n",
      "lossl :  0.0 loss1 :  3.318786548334174e-05 loss2 :  0.0004544258117675781 loss3 :  0.00036907196044921875\n",
      "loss4 :  0.0026292800903320312 loss5 :  0.0012238502968102694\n",
      "time taken :  0.5815742015838623\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0003567695966921747\n",
      "lossl :  0.0 loss1 :  8.78334030858241e-05 loss2 :  0.00012454987154342234 loss3 :  1.964569128176663e-05\n",
      "loss4 :  1.544952465337701e-05 loss5 :  0.00010929107520496473\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0026057243812829256\n",
      "lossl :  0.0 loss1 :  8.420944504905492e-05 loss2 :  0.00014829635620117188 loss3 :  0.0003292083856649697\n",
      "loss4 :  0.0012048721546307206 loss5 :  0.0008391380542889237\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0005948066827841103\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00010461806959938258 loss2 :  8.258819434558973e-05 loss3 :  0.00031156541081145406\n",
      "loss4 :  7.25746140233241e-05 loss5 :  2.326965295651462e-05\n",
      "time taken :  0.19096016883850098\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 194/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.08605832606554031\n",
      "lossl :  0.0 loss1 :  0.0001565933198435232 loss2 :  0.0001316070556640625 loss3 :  0.0006581306224688888\n",
      "loss4 :  0.07986774295568466 loss5 :  0.005244255065917969\n",
      "Iteration :  4  /  7\n",
      "loss :  0.003964900970458984\n",
      "lossl :  1.9073486612342094e-07 loss1 :  1.106262243411038e-05 loss2 :  2.765655517578125e-05 loss3 :  0.0021064758766442537\n",
      "loss4 :  0.0005047798040322959 loss5 :  0.0013147353893145919\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0954139307141304\n",
      "lossl :  0.0 loss1 :  0.0002208709775004536 loss2 :  0.0029905319679528475 loss3 :  0.0004875183221884072\n",
      "loss4 :  0.09088893234729767 loss5 :  0.0008260727045126259\n",
      "time taken :  0.5778729915618896\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0010605811839923263\n",
      "lossl :  0.0 loss1 :  1.5163421267061494e-05 loss2 :  4.38690185546875e-05 loss3 :  0.0008015632629394531\n",
      "loss4 :  3.06129441014491e-05 loss5 :  0.0001693725644145161\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00027866364689543843\n",
      "lossl :  0.0 loss1 :  3.414153979974799e-05 loss2 :  5.035400317865424e-05 loss3 :  1.296997106692288e-05\n",
      "loss4 :  0.00010719299461925402 loss5 :  7.400512549793348e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00069599156267941\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0003829956112895161 loss2 :  0.00015344619168899953 loss3 :  8.18252592580393e-05\n",
      "loss4 :  1.182556115963962e-05 loss5 :  6.570816185558215e-05\n",
      "time taken :  0.18707966804504395\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 195/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration :  1  /  7\n",
      "loss :  0.001973056932911277\n",
      "lossl :  0.0 loss1 :  0.0014561653370037675 loss2 :  2.2983551389188506e-05 loss3 :  9.956360008800402e-05\n",
      "loss4 :  0.00031938552274368703 loss5 :  7.495879981433973e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.029483653604984283\n",
      "lossl :  0.0 loss1 :  0.00028505324735306203 loss2 :  0.019494056701660156 loss3 :  0.003302669618278742\n",
      "loss4 :  0.00028934478177689016 loss5 :  0.006112528033554554\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0006170272827148438\n",
      "lossl :  0.0 loss1 :  2.7561187380342744e-05 loss2 :  0.00011749267287086695 loss3 :  5.931854320806451e-05\n",
      "loss4 :  4.978180004400201e-05 loss5 :  0.0003628730773925781\n",
      "time taken :  0.5819618701934814\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0009797096718102694\n",
      "lossl :  0.0 loss1 :  3.728866431629285e-05 loss2 :  4.38690176451928e-06 loss3 :  1.296997106692288e-05\n",
      "loss4 :  0.00046939851017668843 loss5 :  0.00045566557673737407\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0006023406749591231\n",
      "lossl :  0.0 loss1 :  3.2711028325138614e-05 loss2 :  0.0002305984526174143 loss3 :  8.249282836914062e-05\n",
      "loss4 :  0.00019826888456009328 loss5 :  5.826949927723035e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.001280784490518272\n",
      "lossl :  0.0 loss1 :  1.1157989320054185e-05 loss2 :  3.61442580469884e-05 loss3 :  0.0010181426769122481\n",
      "loss4 :  8.20159948489163e-06 loss5 :  0.00020713805861305445\n",
      "time taken :  0.18803977966308594\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 196/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.008236408233642578\n",
      "lossl :  2.19345088225964e-06 loss1 :  8.59260544530116e-05 loss2 :  0.00035037993802689016 loss3 :  0.005146122071892023\n",
      "loss4 :  0.0003798484685830772 loss5 :  0.002271938370540738\n",
      "Iteration :  4  /  7\n",
      "loss :  0.01332864724099636\n",
      "lossl :  0.0 loss1 :  0.0002038955717580393 loss2 :  0.00110626220703125 loss3 :  0.011860370635986328\n",
      "loss4 :  0.00012874603271484375 loss5 :  2.937316821771674e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.10140953212976456\n",
      "lossl :  0.0 loss1 :  0.0001770019589457661 loss2 :  0.09752941131591797 loss3 :  0.0002612113894429058\n",
      "loss4 :  0.0032579421531409025 loss5 :  0.00018396376981399953\n",
      "time taken :  0.6100752353668213\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0007683754083700478\n",
      "lossl :  0.0 loss1 :  8.583068620282575e-07 loss2 :  0.00043745042057707906 loss3 :  7.572174217784777e-05\n",
      "loss4 :  0.00013475418381858617 loss5 :  0.00011959076073253527\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00023994444927666336\n",
      "lossl :  0.0 loss1 :  4.539489600574598e-05 loss2 :  1.7452239262638614e-05 loss3 :  7.133484177757055e-05\n",
      "loss4 :  6.971359107410535e-05 loss5 :  3.604888843256049e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0193463321775198\n",
      "lossl :  0.0 loss1 :  3.643035961431451e-05 loss2 :  0.00125713343732059 loss3 :  0.012576675042510033\n",
      "loss4 :  0.005458545871078968 loss5 :  1.754760705807712e-05\n",
      "time taken :  0.19350028038024902\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 197/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.05099930986762047\n",
      "lossl :  2.86102294921875e-06 loss1 :  0.006715631578117609 loss2 :  0.002512168837711215 loss3 :  0.0007380485767498612\n",
      "loss4 :  0.000826930976472795 loss5 :  0.04020366817712784\n",
      "Iteration :  4  /  7\n",
      "loss :  0.004974555689841509\n",
      "lossl :  0.0 loss1 :  0.00017881393432617188 loss2 :  1.144409225162235e-06 loss3 :  0.000732421875\n",
      "loss4 :  0.002746009733527899 loss5 :  0.0013161659007892013\n",
      "Iteration :  7  /  7\n",
      "loss :  0.22386206686496735\n",
      "lossl :  0.0 loss1 :  4.57763690064894e-06 loss2 :  0.0032270909287035465 loss3 :  0.0005279540782794356\n",
      "loss4 :  0.013717937283217907 loss5 :  0.20638450980186462\n",
      "time taken :  0.5768847465515137\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.012794590555131435\n",
      "lossl :  0.0 loss1 :  4.76837158203125e-06 loss2 :  0.000476837158203125 loss3 :  0.009111404418945312\n",
      "loss4 :  0.003142738249152899 loss5 :  5.8841706049861386e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0007143020047806203\n",
      "lossl :  6.675720101156912e-07 loss1 :  0.00051031110342592 loss2 :  0.00011119842383777723 loss3 :  2.746581958490424e-05\n",
      "loss4 :  2.441406286379788e-05 loss5 :  4.024505687993951e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00018320084200240672\n",
      "lossl :  0.0 loss1 :  2.651214526849799e-05 loss2 :  2.136230432370212e-05 loss3 :  5.626678466796875e-05\n",
      "loss4 :  4.7016143071232364e-05 loss5 :  3.204345557605848e-05\n",
      "time taken :  0.1863245964050293\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 198/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.011352729983627796\n",
      "lossl :  0.0 loss1 :  0.003878307295963168 loss2 :  0.0023088455200195312 loss3 :  0.0012854576343670487\n",
      "loss4 :  0.0032321929465979338 loss5 :  0.0006479263538494706\n",
      "Iteration :  4  /  7\n",
      "loss :  0.00045070648775435984\n",
      "lossl :  0.0 loss1 :  5.626678557746345e-06 loss2 :  2.2602082026423886e-05 loss3 :  1.220703143189894e-05\n",
      "loss4 :  1.621246337890625e-05 loss5 :  0.0003940582391805947\n",
      "Iteration :  7  /  7\n",
      "loss :  0.028830528259277344\n",
      "lossl :  0.0 loss1 :  3.814697322468419e-07 loss2 :  2.098083541568485e-06 loss3 :  3.757476952159777e-05\n",
      "loss4 :  0.02874298021197319 loss5 :  4.749298022943549e-05\n",
      "time taken :  0.5768489837646484\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.00010023116919910535\n",
      "lossl :  0.0 loss1 :  3.709793236339465e-05 loss2 :  2.994537317135837e-05 loss3 :  5.91278057981981e-06\n",
      "loss4 :  1.468658410885837e-05 loss5 :  1.258850079466356e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  6.341934204101562e-05\n",
      "lossl :  0.0 loss1 :  1.1730194273695815e-05 loss2 :  1.544952465337701e-05 loss3 :  2.002716064453125e-05\n",
      "loss4 :  3.623962356869015e-06 loss5 :  1.258850079466356e-05\n",
      "Iteration :  7  /  7\n",
      "loss :  0.00020647048950195312\n",
      "lossl :  0.0 loss1 :  7.629394644936838e-07 loss2 :  1.6880036127986386e-05 loss3 :  1.277923547604587e-05\n",
      "loss4 :  0.00011587142944335938 loss5 :  6.017684791004285e-05\n",
      "time taken :  0.18821406364440918\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 199/199\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.09190263599157333\n",
      "lossl :  0.0 loss1 :  0.0 loss2 :  0.09166021645069122 loss3 :  2.365112231927924e-05\n",
      "loss4 :  4.57763671875e-05 loss5 :  0.00017299651517532766\n",
      "Iteration :  4  /  7\n",
      "loss :  0.6723756790161133\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.0001142501860158518 loss2 :  0.07588796317577362 loss3 :  0.02789893187582493\n",
      "loss4 :  0.011464500799775124 loss5 :  0.5570098161697388\n",
      "Iteration :  7  /  7\n",
      "loss :  0.01259546261280775\n",
      "lossl :  0.0 loss1 :  0.00017766952805686742 loss2 :  0.0005952835199423134 loss3 :  0.0037611008156090975\n",
      "loss4 :  0.00140380859375 loss5 :  0.006657600402832031\n",
      "time taken :  0.5713114738464355\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  7\n",
      "loss :  0.0001011848435155116\n",
      "lossl :  0.0 loss1 :  5.016326758777723e-05 loss2 :  2.3746490114717744e-05 loss3 :  1.1444091796875e-05\n",
      "loss4 :  4.38690176451928e-06 loss5 :  1.1444091796875e-05\n",
      "Iteration :  4  /  7\n",
      "loss :  0.0010884285438805819\n",
      "lossl :  0.0 loss1 :  1.125335711549269e-05 loss2 :  5.91278076171875e-05 loss3 :  1.697540210443549e-05\n",
      "loss4 :  0.00026998520479537547 loss5 :  0.0007310867076739669\n",
      "Iteration :  7  /  7\n",
      "loss :  0.0003444671747274697\n",
      "lossl :  1.9073486612342094e-07 loss1 :  0.00012636184692382812 loss2 :  2.689361645025201e-05 loss3 :  7.352828833973035e-05\n",
      "loss4 :  7.05718994140625e-05 loss5 :  4.692077709478326e-05\n",
      "time taken :  0.19045448303222656\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(num_epochs):  # loop over the dataset multiple times\n",
    "    \n",
    "    print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "    print('-' * 110)\n",
    "    \n",
    "    # Each epoch has a training and validation phase\n",
    "    for phase in ['train', 'val']:\n",
    "        \n",
    "        if phase == 'train':\n",
    "            net.train(True)  # Set model to training mode\n",
    "            print(\"train phase\")\n",
    "        else:\n",
    "            net.train(False)  # Set model to evaluate mode\n",
    "            print(\"evaluate phase\")\n",
    "\n",
    "        i = 0\n",
    "        rng_state = torch.get_rng_state()\n",
    "        new_idxs = torch.randperm(num_train)\n",
    "        c5_X = c5_data_tensor[new_idxs]\n",
    "        c5_Y = c5_target_tensor[new_idxs]\n",
    "\n",
    "        t1 = time.time()\n",
    "        for t in range(iter_per_epoch):\n",
    "\n",
    "            X_batch = c5_X[i: i+batch_size]\n",
    "            Y_batch = c5_Y[i: i+batch_size]\n",
    "            i += batch_size\n",
    "\n",
    "            Y_batch = Variable(Y_batch.cuda())\n",
    "            X_batch = Variable(X_batch.cuda())\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            outputs = net(X_batch)\n",
    "\n",
    "            lossl = objective(outputs[0], Y_batch[:, 0])\n",
    "            loss1 = objective(outputs[1], Y_batch[:, 1])\n",
    "            loss2 = objective(outputs[2], Y_batch[:, 2])\n",
    "            loss3 = objective(outputs[3], Y_batch[:, 3])\n",
    "            loss4 = objective(outputs[4], Y_batch[:, 4])\n",
    "            loss5 = objective(outputs[5], Y_batch[:, 5])\n",
    "            final_loss = lossl + loss1 + loss2 + loss3 + loss4 + loss5\n",
    "            \n",
    "            if phase == 'train':\n",
    "                final_loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "            loss_history.append(final_loss.data[0])\n",
    "            epoch_losses[epoch].append(final_loss.data[0])\n",
    "\n",
    "            if (t % print_every == 0):\n",
    "                print('Iteration : ', t+1, ' / ', iter_per_epoch)\n",
    "                print('loss : ', final_loss.data[0])\n",
    "                print('lossl : ', lossl.data[0], 'loss1 : ', loss1.data[0], 'loss2 : ', loss2.data[0], 'loss3 : ', loss3.data[0])\n",
    "                print('loss4 : ', loss4.data[0], 'loss5 : ', loss5.data[0])\n",
    "        t2 = time.time()\n",
    "        print(\"time taken : \", t2-t1)\n",
    "        print('-' * 110)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(\"c5.pkl\", \"bw\")\n",
    "torch.save(net.state_dict(), f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe5fc0fdac8>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bi7YWhnX+Vodq+3fhauJ0OBQOVzibcuqbzF+Qhgk9gTz3i4tw7hk92l6nTTkX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\niYhsggmdiMgm/g+jEwAiMssYmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe606f1b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(loss_history)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
